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  • 1.
    Al Moubayed, Samer
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Prosodic Disambiguation in Spoken Systems Output2009In: Proceedings of Diaholmia'09: 2009 Workshop on the Semantics and Pragmatics of Dialogue / [ed] Jens Edlund, Joakim Gustafson, Anna Hjalmarsson, Gabriel Skantze, Stockholm, Sweden., 2009, p. 131-132Conference paper (Refereed)
    Abstract [en]

    This paper presents work on using prosody in the output of spoken dialogue systems to resolve possible structural ambiguity of output utterances. An algorithm is proposed to discover ambiguous parses of an utterance and to add prosodic disambiguation events to deliver the intended structure. By conducting a pilot experiment, the automatic prosodic grouping applied to ambiguous sentences shows the ability to deliver the intended interpretation of the sentences.

  • 2.
    Al Moubayed, Samer
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Ananthakrishnan, Gopal
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Enflo, Laura
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Music Acoustics. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Automatic Prominence Classification in Swedish2010In: Proceedings of Speech Prosody 2010, Workshop on Prosodic Prominence, Chicago, USA, 2010Conference paper (Refereed)
    Abstract [en]

    This study aims at automatically classifying levels of acoustic prominence on a dataset of 200 Swedish sentences of read speech by one male native speaker. Each word in the sentences was categorized by four speech experts into one of three groups depending on the level of prominence perceived. Six acoustic features at a syllable level and seven features at a word level were used. Two machine learning algorithms, namely Support Vector Machines (SVM) and memory based Learning (MBL) were trained to classify the sentences into their respective classes. The MBL gave an average word level accuracy of 69.08% and the SVM gave an average accuracy of 65.17 % on the test set. These values were comparable with the average accuracy of the human annotators with respect to the average annotations. In this study, word duration was found to be the most important feature required for classifying prominence in Swedish read speech

  • 3.
    Al Moubayed, Samer
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    Baklouti, M.
    Chetouani, M.
    Dutoit, T.
    Mahdhaoui, A.
    Martin, J. -C
    Ondas, S.
    Pelachaud, C.
    Urbain, J.
    Yilmaz, M.
    Generating Robot/Agent Backchannels During a Storytelling Experiment: 2009 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-72009In: ICRA: 2009 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, 2009, p. 3749-3754Conference paper (Refereed)
    Abstract [en]

    This work presents the development of a real-time framework for the research of Multimodal Feedback of Robots/Talking Agents in the context of Human Robot Interaction (HRI) and Human Computer Interaction (HCI). For evaluating the framework, a Multimodal corpus is built (ENTERFACE_STEAD), and a study on the important multimodal features was done for building an active Robot/Agent listener of a storytelling experience with Humans. The experiments show that even when building the same reactive behavior models for Robot and Talking Agents, the interpretation and the realization of the behavior communicated is different due to the different communicative channels Robots/Agents offer be it physical but less-human-like in Robots, and virtual but more expressive and human-like in Talking agents.

  • 4.
    Al Moubayed, Samer
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Beskow, Jonas
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Effects of Visual Prominence Cues on Speech Intelligibility2009In: Proceedings of Auditory-Visual Speech Processing AVSP'09, Norwich, England, 2009Conference paper (Refereed)
    Abstract [en]

    This study reports experimental results on the effect of visual prominence, presented as gestures, on speech intelligibility. 30 acoustically vocoded sentences, permutated into different gestural conditions were presented audio-visually to 12 subjects. The analysis of correct word recognition shows a significant increase in intelligibility when focally-accented (prominent) words are supplemented with head-nods or with eye-brow raise gestures. The paper also examines coupling other acoustic phenomena to brow-raise gestures. As a result, the paper introduces new evidence on the ability of the non-verbal movements in the visual modality to support audio-visual speech perception.

  • 5.
    Al Moubayed, Samer
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Beskow, Jonas
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Granström, Björn
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Auditory visual prominence From intelligibility to behavior2009In: Journal on Multimodal User Interfaces, ISSN 1783-7677, E-ISSN 1783-8738, Vol. 3, no 4, p. 299-309Article in journal (Refereed)
    Abstract [en]

    Auditory prominence is defined as when an acoustic segment is made salient in its context. Prominence is one of the prosodic functions that has been shown to be strongly correlated with facial movements. In this work, we investigate the effects of facial prominence cues, in terms of gestures, when synthesized on animated talking heads. In the first study, a speech intelligibility experiment is conducted, speech quality is acoustically degraded and the fundamental frequency is removed from the signal, then the speech is presented to 12 subjects through a lip synchronized talking head carrying head-nods and eyebrows raise gestures, which are synchronized with the auditory prominence. The experiment shows that presenting prominence as facial gestures significantly increases speech intelligibility compared to when these gestures are randomly added to speech. We also present a follow-up study examining the perception of the behavior of the talking heads when gestures are added over pitch accents. Using eye-gaze tracking technology and questionnaires on 10 moderately hearing impaired subjects, the results of the gaze data show that users look at the face in a similar fashion to when they look at a natural face when gestures are coupled with pitch accents opposed to when the face carries no gestures. From the questionnaires, the results also show that these gestures significantly increase the naturalness and the understanding of the talking head.

  • 6.
    Al Moubayed, Samer
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Beskow, Jonas
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Salvi, Giampiero
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    SynFace Phone Recognizer for Swedish Wideband and Narrowband Speech2008In: Proceedings of The second Swedish Language Technology Conference (SLTC), Stockholm, Sweden., 2008, p. 3-6Conference paper (Other academic)
    Abstract [en]

    In this paper, we present new results and comparisons of the real-time lips synchronized talking head SynFace on different Swedish databases and bandwidth. The work involves training SynFace on narrow-band telephone speech from the Swedish SpeechDat, and on the narrow-band and wide-band Speecon corpus. Auditory perceptual tests are getting established for SynFace as an audio visual hearing support for the hearing-impaired. Preliminary results show high recognition accuracy compared to other languages.

  • 7.
    Al Moubayed, Samer
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Beskow, Jonas
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Öster, Anne-Marie
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Salvi, Giampiero
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Granström, Björn
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    van Son, Nic
    Viataal, Nijmegen, The Netherlands.
    Ormel, Ellen
    Viataal, Nijmegen, The Netherlands.
    Herzke, Tobias
    HörTech gGmbH, Germany.
    Studies on Using the SynFace Talking Head for the Hearing Impaired2009In: Proceedings of Fonetik'09: The XXIIth Swedish Phonetics Conference, June 10-12, 2009 / [ed] Peter Branderud, Hartmut Traunmüller, Stockholm: Stockholm University, 2009, p. 140-143Conference paper (Other academic)
    Abstract [en]

    SynFace is a lip-synchronized talking agent which is optimized as a visual reading support for the hearing impaired. In this paper wepresent the large scale hearing impaired user studies carried out for three languages in the Hearing at Home project. The user tests focuson measuring the gain in Speech Reception Threshold in Noise and the effort scaling when using SynFace by hearing impaired people, where groups of hearing impaired subjects with different impairment levels from mild to severe and cochlear implants are tested. Preliminaryanalysis of the results does not show significant gain in SRT or in effort scaling. But looking at large cross-subject variability in both tests, it isclear that many subjects benefit from SynFace especially with speech with stereo babble.

  • 8.
    Al Moubayed, Samer
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Beskow, Jonas
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Öster, Ann-Marie
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Salvi, Giampiero
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Granström, Björn
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    van Son, Nic
    Ormel, Ellen
    Virtual Speech Reading Support for Hard of Hearing in a Domestic Multi-Media Setting2009In: INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, BAIXAS: ISCA-INST SPEECH COMMUNICATION ASSOC , 2009, p. 1443-1446Conference paper (Refereed)
    Abstract [en]

    In this paper we present recent results on the development of the SynFace lip synchronized talking head towards multilinguality, varying signal conditions and noise robustness in the Hearing at Home project. We then describe the large scale hearing impaired user studies carried out for three languages. The user tests focus on measuring the gain in Speech Reception Threshold in Noise when using SynFace, and on measuring the effort scaling when using SynFace by hearing impaired people. Preliminary analysis of the results does not show significant gain in SRT or in effort scaling. But looking at inter-subject variability, it is clear that many subjects benefit from SynFace especially with speech with stereo babble noise.

  • 9.
    Al Moubayed, Samer
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    De Smet, Michael
    Van Hamme, Hugo
    Lip Synchronization: from Phone Lattice to PCA Eigen-projections using Neural Networks2008In: INTERSPEECH 2008: 9TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2008, BAIXAS: ISCA-INST SPEECH COMMUNICATION ASSOC , 2008, p. 2016-2019Conference paper (Refereed)
    Abstract [en]

    Lip synchronization is the process of generating natural lip movements from a speech signal. In this work we address the lip-sync problem using an automatic phone recognizer that generates a phone lattice carrying posterior probabilities. The acoustic feature vector contains the posterior probabilities of all the phones over a time window centered at the current time point. Hence this representation characterizes the phone recognition output including the confusion patterns caused by its limited accuracy. A 3D face model with varying texture is computed by analyzing a video recording of the speaker using a 3D morphable model. Training a neural network using 30 000 data vectors from an audiovisual recording in Dutch resulted in a very good simulation of the face on independent data sets of the same or of a different speaker.

  • 10. Allwood, Jens
    et al.
    Cerrato, Loredana
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Jokinen, Kristiina
    Navarretta, Costanza
    Paggio, Patrizia
    The MUMIN coding scheme for the annotation of feedback, turn management and sequencing phenomena2007In: Language resources and evaluation, ISSN 1574-020X, E-ISSN 1574-0218, Vol. 41, no 3-4, p. 273-287Article in journal (Refereed)
    Abstract [en]

    This paper deals with a multimodal annotation scheme dedicated to the study of gestures in interpersonal communication, with particular regard to the role played by multimodal expressions for feedback, turn management and sequencing. The scheme has been developed under the framework of the MUMIN network and tested on the analysis of multimodal behaviour in short video clips in Swedish, Finnish and Danish. The preliminary results obtained in these studies show that the reliability of the categories defined in the scheme is acceptable, and that the scheme as a whole constitutes a versatile analysis tool for the study of multimodal communication behaviour.

  • 11.
    Ananthakrishnan, Gopal
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Badin, P.
    GIPSA-Lab, Grenoble University.
    Vargas, J. A. V.
    GIPSA-Lab, Grenoble University.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Predicting Unseen Articulations from Multi-speaker Articulatory Models2010In: Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010, Makuhari, Japan, 2010, p. 1588-1591Conference paper (Refereed)
    Abstract [en]

    In order to study inter-speaker variability, this work aims to assessthe generalization capabilities of data-based multi-speakerarticulatory models. We use various three-mode factor analysistechniques to model the variations of midsagittal vocal tractcontours obtained from MRI images for three French speakersarticulating 73 vowels and consonants. Articulations of agiven speaker for phonemes not present in the training set arethen predicted by inversion of the models from measurementsof these phonemes articulated by the other subjects. On the average,the prediction RMSE was 5.25 mm for tongue contours,and 3.3 mm for 2D midsagittal vocal tract distances. Besides,this study has established a methodology to determine the optimalnumber of factors for such models.

  • 12.
    Ananthakrishnan, Gopal
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Important regions in the articulator trajectory2008In: Proceedings of International Seminar on Speech Production / [ed] Rudolph Sock, Susanne Fuchs, Yves Laprie, Strasbourg, France: INRIA , 2008, p. 305-308Conference paper (Refereed)
    Abstract [en]

    This paper deals with identifying important regions in the articulatory trajectory based on the physical properties of the trajectory. A method to locate critical time instants as well as the key articulator positions is suggested. Acoustic-to-Articulatory Inversion using linear and non-linear regression isperformed using only these critical points. The accuracy of inversion is found to be almost the same as using all the data points.

  • 13.
    Ananthakrishnan, Gopal
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    Mapping between acoustic and articulatory gestures2011In: Speech Communication, ISSN 0167-6393, E-ISSN 1872-7182, Vol. 53, no 4, p. 567-589Article in journal (Refereed)
    Abstract [en]

    This paper proposes a definition for articulatory as well as acoustic gestures along with a method to segment the measured articulatory trajectories and acoustic waveforms into gestures. Using a simultaneously recorded acoustic-articulatory database, the gestures are detected based on finding critical points in the utterance, both in the acoustic and articulatory representations. The acoustic gestures are parameterized using 2-D cepstral coefficients. The articulatory trajectories arc essentially the horizontal and vertical movements of Electromagnetic Articulography (EMA) coils placed on the tongue, jaw and lips along the midsagittal plane. The articulatory movements are parameterized using 2D-DCT using the same transformation that is applied on the acoustics. The relationship between the detected acoustic and articulatory gestures in terms of the timing as well as the shape is studied. In order to study this relationship further, acoustic-to-articulatory inversion is performed using GMM-based regression. The accuracy of predicting the articulatory trajectories from the acoustic waveforms are at par with state-of-the-art frame-based methods with dynamical constraints (with an average error of 1.45-1.55 mm for the two speakers in the database). In order to evaluate the acoustic-to-articulatory inversion in a more intuitive manner, a method based on the error in estimated critical points is suggested. Using this method, it was noted that the estimated articulatory trajectories using the acoustic-to-articulatory inversion methods were still not accurate enough to be within the perceptual tolerance of audio-visual asynchrony.

  • 14.
    Ananthakrishnan, Gopal
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Resolving Non-uniqueness in the Acoustic-to-Articulatory Mapping2011In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Prague, Czech republic, 2011, p. 4628-4631Conference paper (Refereed)
    Abstract [en]

    This paper studies the role of non-uniqueness in the Acoustic-to- Articulatory Inversion. It is generally believed that applying continuity constraints to the estimates of thearticulatory parameters can resolve the problem of non-uniqueness. This paper tries to find out whether all instances of non-uniqueness can be resolved using continuity constraints. The investigation reveals that applying continuity constraints provides the best estimate in roughly around 50 to 53 % of the non-unique mappings. Roughly around 8 to13 % of the non-unique mappings are best estimated by choosing discontinuous paths along the hypothetical high probability estimates of articulatory trajectories.

  • 15.
    Ananthakrishnan, Gopal
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Salvi, Giampiero
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Using Imitation to learn Infant-Adult Acoustic Mappings2011In: 12th Annual Conference Of The International Speech Communication Association 2011 (INTERSPEECH 2011), Vols 1-5, ISCA , 2011, p. 772-775Conference paper (Refereed)
    Abstract [en]

    This paper discusses a model which conceptually demonstrates how infants could learn the normalization between infant-adult acoustics. The model proposes that the mapping can be inferred from the topological correspondences between the adult and infant acoustic spaces, that are clustered separately in an unsupervised manner. The model requires feedback from the adult in order to select the right topology for clustering, which is a crucial aspect of the model. The feedback Is in terms of an overall rating of the imitation effort by the infant, rather than a frame-by-frame correspondence. Using synthetic, but continuous speech data, we demonstrate that clusters, which have a good topological correspondence, are perceived to be similar by a phonetically trained listener.

  • 16.
    Ananthakrishnan, Gopal
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Wik, Preben
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Detecting confusable phoneme pairs for Swedish language learners depending on their first language2011In: TMH-QPSR, ISSN 1104-5787, Vol. 51, no 1, p. 89-92Article in journal (Other academic)
    Abstract [en]

    This paper proposes a paradigm where commonly made segmental pronunciation errors are modeled as pair-wise confusions between two or more phonemes in the language that is being learnt. The method uses an ensemble of support vector machine classifiers with time varying Mel frequency cepstral features to distinguish between several pairs of phonemes. These classifiers are then applied to classify the phonemes uttered by second language learners. Using this method, an assessment is made regarding the typical pronunciation problems that students learning Swedish would encounter, depending on their first language.

  • 17.
    Ananthakrishnan, Gopal
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Wik, Preben
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Abdou, Sherif
    Faculty of Computers & Information, Cairo University, Egypt.
    Using an Ensemble of Classifiers for Mispronunciation Feedback2011In: Proceedings of SLaTE / [ed] Strik, H.; Delmonte, R.; Russel, M., Venice, Italy, 2011Conference paper (Refereed)
    Abstract [en]

    This paper proposes a paradigm where commonly made segmental pronunciation errors are modeled as pair-wise confusions between two or more phonemes in the language that is being learnt. The method uses an ensemble of support vector machine classifiers with time varying Mel frequency cepstral features to distinguish between several pairs of phonemes. These classifiers are then applied to classify the phonemes uttered by second language learners. Instead of providing feedback at every mispronounced phoneme, the method attempts toprovide feedback about typical mispronunciations by a certain student, over an entire session of several utterances. Two case studies that demonstrate how the paradigm is applied to provide suitable feedback to two students is also described in this pape

  • 18.
    Beskow, Jonas
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Edlund, Jens
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Granström, Björn
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Gustafson, Joakim
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Jonsson, Oskar
    Skantze, Gabriel
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Speech technology in the European project MonAMI2008In: Proceedings of FONETIK 2008 / [ed] Anders Eriksson, Jonas Lindh, Gothenburg, Sweden: University of Gothenburg , 2008, p. 33-36Conference paper (Other academic)
    Abstract [en]

    This paper describes the role of speech and speech technology in the European project MonAMI, which aims at “mainstreaming ac-cessibility in consumer goods and services, us-ing advanced technologies to ensure equal ac-cess, independent living and participation for all”. It presents the Reminder, a prototype em-bodied conversational agent (ECA) which helps users to plan activities and to remember what to do. The prototype merges speech technology with other, existing technologies: Google Cal-endar and a digital pen and paper. The solution allows users to continue using a paper calendar in the manner they are used to, whilst the ECA provides notifications on what has been written in the calendar. Users may also ask questions such as “When was I supposed to meet Sara?” or “What’s on my schedule today?”

  • 19.
    Beskow, Jonas
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    Edlund, Jens
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    Nordstrand, Magnus
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    A Model for Multimodal Dialogue System Output Applied to an Animated Talking Head2005In: SPOKEN MULTIMODAL HUMAN-COMPUTER DIALOGUE IN MOBILE ENVIRONMENTS / [ed] Minker, Wolfgang; Bühler, Dirk; Dybkjær, Laila, Dordrecht: Springer , 2005, p. 93-113Chapter in book (Refereed)
    Abstract [en]

    We present a formalism for specifying verbal and non-verbal output from a multimodal dialogue system. The output specification is XML-based and provides information about communicative functions of the output, without detailing the realisation of these functions. The aim is to let dialogue systems generate the same output for a wide variety of output devices and modalities. The formalism was developed and implemented in the multimodal spoken dialogue system AdApt. We also describe how facial gestures in the 3D-animated talking head used within this system are controlled through the formalism.

  • 20.
    Beskow, Jonas
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    Granström, Björn
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    Nordqvist, Peter
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Al Moubayed, Samer
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    Salvi, Giampiero
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    Herzke, Tobias
    Schulz, Arne
    Hearing at Home: Communication support in home environments for hearing impaired persons2008In: INTERSPEECH 2008: 9TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2008, BAIXAS: ISCA-INST SPEECH COMMUNICATION ASSOC , 2008, p. 2203-2206Conference paper (Refereed)
    Abstract [en]

    The Hearing at Home (HaH) project focuses on the needs of hearing-impaired people in home environments. The project is researching and developing an innovative media-center solution for hearing support, with several integrated features that support perception of speech and audio, such as individual loudness amplification, noise reduction, audio classification and event detection, and the possibility to display an animated talking head providing real-time speechreading support. In this paper we provide a brief project overview and then describe some recent results related to the audio classifier and the talking head. As the talking head expects clean speech input, an audio classifier has been developed for the task of classifying audio signals as clean speech, speech in noise or other. The mean accuracy of the classifier was 82%. The talking head (based on technology from the SynFace project) has been adapted for German, and a small speech-in-noise intelligibility experiment was conducted where sentence recognition rates increased from 3% to 17% when the talking head was present.

  • 21.
    Bälter, Olle
    et al.
    KTH, School of Computer Science and Communication (CSC), Human - Computer Interaction, MDI.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Öster, Anne-Marie
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Kjellström, Hedvig
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Wizard-of-Oz Test of ARTUR - a Computer-Based Speech Training System with Articulation Correction2005In: proceedings of ASSETS 2005, 2005, p. 36-43Conference paper (Refereed)
    Abstract [en]

    This study has been performed in order to test the human-machine interface of a computer-based speech training aid named ARTUR with the main feature that it can give suggestions on how to improve articulation. Two user groups were involved: three children aged 9-14 with extensive experience of speech training, and three children aged 6. All children had general language disorders. The study indicates that the present interface is usable without prior training or instructions, even for the younger children, although it needs some improvement to fit illiterate children. The granularity of the mesh that classifies mispronunciations was satisfactory, but can be developed further.

  • 22.
    Carlson, Rolf
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Hirschberg, Julia
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Cross-Cultural Perception of Discourse Phenomena2009In: INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, BAIXAS: ISCA-INST SPEECH COMMUNICATION ASSOC , 2009, p. 1723-1726Conference paper (Refereed)
    Abstract [en]

    We discuss perception studies of two low level indicators of discourse phenomena by Swedish. Japanese, and Chinese native speakers. Subjects were asked to identify upcoming prosodic boundaries and disfluencies in Swedish spontaneous speech. We hypothesize that speakers of prosodically unrelated languages should be less able to predict upcoming phrase boundaries but potentially better able to identify disfluencies, since indicators of disfluency are more likely to depend upon lexical, as well as acoustic information. However, surprisingly, we found that both phenomena were fairly well recognized by native and non-native speakers, with, however, some possible interference from word tones for the Chinese subjects.

  • 23.
    Edlund, Jens
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Heldner, Mattias
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Gustafson, Joakim
    Voice Technologies, Expert Functions, Teliasonera, Haninge, Sweden.
    Utterance segmentation and turn-taking in spoken dialogue systems2005In: Computer Studies in Language and Speech / [ed] Fisseni, B.; Schmitz, H-C.; Schröder, B.; Wagner, P., Frankfurt am Main, Germany: Peter Lang , 2005, p. 576-587Chapter in book (Refereed)
    Abstract [en]

    A widely used method for finding places to take turn in spoken dialogue systems is to assume that an utterance ends where the user ceases to speak. Such endpoint detection normally triggers on a certain amount of silence, or non-speech. However, spontaneous speech frequently contains silent pauses inside sentencelike units, for example when the speaker hesitates. This paper presents /nailon/, an on-line, real-time prosodic analysis tool, and a number of experiments in which end-point detection has been augmented with prosodic analysis in order to segment the speech signal into what humans intuitively perceive as utterance-like units.

  • 24.
    Edlund, Jens
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Hjalmarsson, Anna
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Applications of distributed dialogue systems: the KTH Connector2005In: Proceedings of ISCA Tutorial and Research Workshop on Applied Spoken Language Interaction in Distributed Environments (ASIDE 2005), 2005Conference paper (Refereed)
    Abstract [en]

    We describe a spoken dialogue system domain: that of the personal secretary. This domain allows us to capitalise on the characteristics that make speech a unique interface; characteristics that humans use regularly, implicitly, and with remarkable ease. We present a prototype system - the KTH Connector - and highlight several dialogue research issues arising in the domain.

  • 25. Eklund, R.
    et al.
    Peters, G.
    Ananthakrishnan, Gopal
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Mabiza, E.
    An acoustic analysis of lion roars. I: Data collection and spectrogram and waveform analyses2011In: TMH-QPSR, ISSN 1104-5787, Vol. 51, no 1, p. 1-4Article in journal (Other academic)
    Abstract [en]

    This paper describes the collection of lion roar data at two different locations, an outdoor setting at Antelope Park in Zimbabwe and an indoor setting at Parken Zoo in Sweden. Preliminary analyses of spectrographic and waveform data are provided.

  • 26.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Articulatory synthesis using corpus-based estimation of line spectrum pairs2005In: 9th European Conference on Speech Communication and Technology, 2005, p. 1909-1912Conference paper (Refereed)
    Abstract [en]

    An attempt to define a new articulatory synthesis method, in which the speech signal is generated through a statistical estimation of its relation with articulatory parameters, is presented. A corpus containing acoustic material and simultaneous recordings of the tongue and facial movements was used to train and test the articulatory synthesis of VCV words and short sentences. Tongue and facial motion data, captured with electromagnetic articulography and three-dimensional optical motion tracking, respectively, define articulatory parameters of a talking head. These articulatory parameters are then used as estimators of the speech signal, represented by line spectrum pairs. The statistical link between the articulatory parameters and the speech signal was established using either linear estimation or artificial neural networks. The results show that the linear estimation was only enough to synthesize identifiable vowels, but not consonants, whereas the neural networks gave a perceptually better synthesis.

  • 27.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    Can audio-visual instructions help learners improve their articulation?: an ultrasound study of short term changes2008In: INTERSPEECH 2008: 9TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2008, BAIXAS: ISCA-INST SPEECH COMMUNICATION ASSOC , 2008, p. 2631-2634Conference paper (Refereed)
    Abstract [en]

    This paper describes how seven French subjects change their pronunciation and articulation when practising Swedish words with a computer-animated virtual teacher. The teacher gives feedback on the user's pronunciation with audiovisual instructions suggesting how the articulation should be changed. A wizard-of-Oz set-up was used for the training session, in which a human listener choose the adequate pre-generated feedback based on the user's pronunciation. The subjects change of the articulation was monitored during the practice session with a hand-held ultrasound probe. The perceptual analysis indicates that the subjects improved their pronunciation during the training and the ultrasound measurements suggest that the improvement was made by following the articulatory instructions given by the computer-animated teacher.

  • 28.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Evaluation of speech inversion using an articulatory classifier2006In: In Proceedings of the Seventh International Seminar on Speech Production / [ed] Yehia, H.; Demolin, D.; Laboissière, R., 2006, p. 469-476Conference paper (Refereed)
    Abstract [en]

    This paper presents an evaluation method for statistically basedspeech inversion, in which the estimated vocal tract shapes are classified intophoneme categories based on the articulatory correspondence with prototypevocal tract shapes. The prototypes are created using the original articulatorydata and the classifier hence permits to interpret the results of the inversion interms of, e.g., confusions between different articulations and the success in estimatingdifferent places of articulation. The articulatory classifier was used toevaluate acoustic and audiovisual speech inversion of VCV words and Swedishsentences performed with a linear estimation and an artificial neural network.

  • 29.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Feedback strategies of human and virtual tutors in pronunciation training2006In: TMH-QPSR, ISSN 1104-5787, Vol. 48, no 1, p. 011-034Article in journal (Other academic)
    Abstract [en]

    This paper presents a survey of language teachers’ and their students’ attitudes and practice concerning the use of corrective feedback in pronunciation training. Theaim of the study is to identify feedback strategies that can be used successfully ina computer assisted pronunciation training system with a virtual tutor giving articulatoryinstructions and feedback. The study was carried out using focus groupmeetings, individual semi-structured interviews and classroom observations. Implicationsfor computer assisted pronunciation training are presented and some havebeen tested with 37 users in a short practice session with a virtual teacher

  • 30.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Introducing visual cues in acoustic-to-articulatory inversion2005In: Interspeech 2005: 9th European Conference on Speech Communication and Technology, 2005, p. 3205-3208Conference paper (Refereed)
    Abstract [en]

    The contribution of facial measures in a statistical acoustic-to- articulatory inversion has been investigated. The tongue contour was estimated using a linear estimation from either acoustics or acoustics and facial measures. Measures of the lateral movement of lip corners and the vertical movement of the upper and lower lip and the jaw gave a substantial improvement over the audio-only case. It was further found that adding the corresponding articulatory measures that could be extracted from a profile view of the face; i.e. the protrusion of the lips, lip corners and the jaw, did not give any additional improvement of the inversion result. The present study hence suggests that audiovisual-to-articulatory inversion can as well be performed using front view monovision of the face, rather than stereovision of both the front and profile view.

  • 31.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Is there a McGurk effect for tongue reading?2010In: Proceedings of AVSP: International Conferenceon Audio-Visual Speech Processing, 2010Conference paper (Refereed)
    Abstract [en]

    Previous studies on tongue reading, i.e., speech perception ofdegraded audio supported by animations of tongue movementshave indicated that the support is weak initially and that subjectsneed training to learn to interpret the movements. Thispaper investigates if the learning is of the animation templatesas such or if subjects learn to retrieve articulatory knowledgethat they already have. Matching and conflicting animationsof tongue movements were presented randomly together withthe auditory speech signal at three different levels of noise in aconsonant identification test. The average recognition rate overthe three noise levels was significantly higher for the matchedaudiovisual condition than for the conflicting and the auditoryonly. Audiovisual integration effects were also found for conflictingstimuli. However, the visual modality is given much lessweight in the perception than for a normal face view, and intersubjectdifferences in the use of visual information are large.

  • 32.
    Engwall, Olov
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    Wik, Preben
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    Are real tongue movements easier to speech read than synthesized?2009In: INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, BAIXAS: ISCA-INST SPEECH COMMUNICATION ASSOC , 2009, p. 824-827Conference paper (Refereed)
    Abstract [en]

    Speech perception studies with augmented reality displays in talking heads have shown that tongue reading abilities are weak initially, but that subjects become able to extract some information from intra-oral visualizations after a short training session. In this study, we investigate how the nature of the tongue movements influences the results, by comparing synthetic rule-based and actual, measured movements. The subjects were significantly better at perceiving sentences accompanied by real movements, indicating that the current coarticulation model developed for facial movements is not optimal for the tongue.

  • 33.
    Engwall, Olov
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Wik, Preben
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Can you tell if tongue movements are real or synthetic?2009In: Proceedings of AVSP, 2009Conference paper (Refereed)
    Abstract [en]

    We have investigated if subjects are aware of what natural tongue movements look like, by showing them animations based on either measurements or rule-based synthesis. The issue is of interest since a previous audiovisual speech perception study recently showed that the word recognition rate in sentences with degraded audio was significantly better with real tongue movements than with synthesized. The subjects in the current study could as a group not tell which movements were real, with a classification score at chance level. About half of the subjects were significantly better at discriminating between the two types of animations, but their classification score was as often well below chance as above. The correlation between classification score and word recognition rate for subjects who also participated in the perception study was very weak, suggesting that the higher recognition score for real tongue movements may be due to subconscious, rather than conscious, processes. This finding could potentially be interpreted as an indication that audiovisual speech perception is based onarticulatory gestures.

  • 34.
    Granström, Björn
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Speech technology for language training and e-inclusion2005Conference paper (Refereed)
    Abstract [en]

    Efficient language learning is one of the keys to social inclusion. In this paper we present some work aiming at creating a virtual language tutor. The ambition is to create a tutor that can be engaged in many aspects of language learning from detailed pronunciation training to conversational practice. Some of the crucial components of such a system are described. An initial implementation of a stress/quantity training tutor for Swedish will be presented.

  • 35.
    Hincks, Rebecca
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Language and Communication.
    Edlund, Jens
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    PROMOTING INCREASED PITCH VARIATION IN ORAL PRESENTATIONS WITH TRANSIENT VISUAL FEEDBACK2009In: Language Learning & Technology, ISSN 1094-3501, E-ISSN 1094-3501, Vol. 13, no 3, p. 32-50Article in journal (Refereed)
    Abstract [en]

    This paper investigates learner response to a novel kind of intonation feedback generated from speech analysis. Instead of displays of pitch curves, our feedback is flashing lights that show how much pitch variation the speaker has produced. The variable used to generate the feedback is the standard deviation of fundamental frequency as measured in semitones. Flat speech causes the system to show yellow lights, while more expressive speech that has used pitch to give focus to any part of an utterance generates green lights. Participants in the study were 14 Chinese students of English at intermediate and advanced levels. A group that received visual feedback was compared with a group that received audio feedback. Pitch variation was measured at four stages: in a baseline oral presentation; for the first and second halves of three hours of training; and finally in the production of a new oral presentation. Both groups increased their pitch variation with training, and the effect lasted after the training had ended. The test group showed a significantly higher increase than the control group, indicating that the feedback is effective. These positive results imply that the feedback could be beneficially used in a system for practicing oral presentations.

  • 36.
    Hjalmarsson, Anna
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Edlund, Jens
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Human-likeness in utterance generation: Effects of variability2008In: Perception In Multimodal Dialogue Systems, Proceedings / [ed] Andre, E; Dybkjaer, L; Minker, W; Neumann, H; Pieraccini, R; Weber, M, 2008, Vol. 5078, p. 252-255Conference paper (Refereed)
    Abstract [en]

    There are compelling reasons to endow dialogue systems with human-like conversational abilities, which require modelling of aspects of human behaviour. This paper examines the value of using human behaviour as a target for system behaviour through a study making use of a simulation method. Two versions of system behaviour are compared: a replica of a human speaker's behaviour and a constrained version with less variability. The version based on human behaviour is rated more human-like, polite and intelligent.

  • 37. Katsamanis, N.
    et al.
    Ananthakrishnan, Gopal
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Papandreou, G.
    Maragos, P.
    NTU, Athens, Greece.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Audiovisual speech inversion by switching dynamical modeling Governed by a Hidden Markov Process2008In: Proceedings of EUSIPCO, 2008Conference paper (Refereed)
    Abstract [en]

    We propose a unified framework to recover articulation from audiovisual speech. The nonlinear audiovisual-to-articulatory mapping is modeled by means of a switching linear dynamical system. Switching is governed by a state sequence determined via a Hidden Markov Model alignment process. Mel Frequency Cepstral Coefficients are extracted from audio while visual analysis is performed using Active Appearance Models. The articulatory state is represented by the coordinates of points on important articulators, e.g., tongue and lips. To evaluate our inversion approach, instead of just using the conventional correlation coefficients and root mean squared errors, we introduce a novel evaluation scheme that is more specific to the inversion problem. Prediction errors in the positions of the articulators are weighted differently depending on their relevant importance in the production of the corresponding sound. The applied weights are determined by an articulatory classification analysis using Support Vector Machines with a radial basis function kernel. Experiments are conducted in the audiovisual-articulatory MOCHA database.

  • 38.
    Kjellström, Hedvig
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    Abdou, Sherif
    Bälter, Olle
    KTH, School of Computer Science and Communication (CSC), Human - Computer Interaction, MDI.
    Audio-visual phoneme classification for pronunciation training applications2007In: INTERSPEECH 2007: 8TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION, BAIXAS: ISCA-INST SPEECH COMMUNICATION ASSOC , 2007, p. 57-60Conference paper (Refereed)
    Abstract [en]

    We present a method for audio-visual classification of Swedish phonemes, to be used in computer-assisted pronunciation training. The probabilistic kernel-based method is applied to the audio signal and/or either a principal or an independent component (PCA or ICA) representation of the mouth region in video images. We investigate which representation (PCA or ICA) that may be most suitable and the number of components required in the base, in order to be able to automatically detect pronunciation errors in Swedish from audio-visual input. Experiments performed on one speaker show that the visual information help avoiding classification errors that would lead to gravely erroneous feedback to the user; that it is better to perform phoneme classification on audio and video separately and then fuse the results, rather than combining them before classification; and that PCA outperforms ICA for fewer than 50 components.

  • 39.
    Kjellström, Hedvig
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH.
    Bälter, Olle
    KTH, School of Computer Science and Communication (CSC), Human - Computer Interaction, MDI.
    Reconstructing Tongue Movements from Audio and Video2006In: INTERSPEECH 2006 AND 9TH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING, Vol. 1-5, BAIXAS: ISCA-INST SPEECH COMMUNICATION ASSOC , 2006, p. 2238-2241Conference paper (Refereed)
    Abstract [en]

    This paper presents an approach to articulatory inversion using audio and video of the user's face, requiring no special markers. The video is stabilized with respect to the face, and the mouth region cropped out. The mouth image is projected into a learned independent component subspace to obtain a low-dimensional representation of the mouth appearance. The inversion problem is treated as one of regression; a non-linear regressor using relevance vector machines is trained with a dataset of simultaneous images of a subject's face, acoustic features and positions of magnetic coils glued to the subjects's tongue. The results show the benefit of using both cues for inversion. We envisage the inversion method to be part of a pronunciation training system with articulatory feedback.

  • 40.
    Koniaris, Christos
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Perceptual differentiation modeling explains phoneme mispronunciation by non-native speakers2011In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2011, p. 5704-5707Conference paper (Refereed)
    Abstract [en]

    One of the difficulties in second language (L2) learning is the weakness in discriminating between acoustic diversity within an L2 phoneme category and between different categories. In this paper, we describe a general method to quantitatively measure the perceptual difference between a group of native and individual non-native speakers. Normally, this task includes subjective listening tests and/or a thorough linguistic study. We instead use a totally automated method based on a psycho-acoustic auditory model. For a certain phoneme class, we measure the similarity of the Euclidean space spanned by the power spectrum of a native speech signal and the Euclidean space spanned by the auditory model output. We do the same for a non-native speech signal. Comparing the two similarity measurements, we find problematic phonemes for a given speaker. To validate our method, we apply it to different groups of non-native speakers of various first language (L1) backgrounds. Our results are verified by the theoretical findings in literature obtained from linguistic studies.

  • 41.
    Koniaris, Christos
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Phoneme Level Non-Native Pronunciation Analysis by an Auditory Model-based Native Assessment Scheme2011In: 12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011, International Speech Communication Association, INTERSPEECH , 2011, p. 1157-1160Conference paper (Refereed)
    Abstract [en]

    We introduce a general method for automatic diagnostic evaluation of the pronunciation of individual non-native speakers based on a model of the human auditory system trained with native data stimuli. For each phoneme class, the Euclidean geometry similarity between the native perceptual domain and the non-native speech power spectrum domain is measured. The problematic phonemes for a given second language speaker are found by comparing this measure to the Euclidean geometry similarity for the same phonemes produced by native speakers only. The method is applied to different groups of non-native speakers of various language backgrounds and the experimental results are in agreement with theoretical findings of linguistic studies.

  • 42. Laskowski, Kornel
    et al.
    Edlund, Jens
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Heldner, Mattias
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    An instantaneous vector representation of delta pitch for speaker-change prediction in conversational dialogue systems2008In: 2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, New York: IEEE , 2008, p. 5041-5044Conference paper (Refereed)
    Abstract [en]

    As spoken dialogue systems become deployed in increasingly complex domains, they face rising demands on the naturalness of interaction. We focus on system responsiveness, aiming to mimic human-like dialogue flow control by predicting speaker changes as observed in real human-human conversations. We derive an instantaneous vector representation of pitch variation and show that it isamenable to standard acoustic modeling techniques. Using a small amount of automatically labeled data, we train models which significantly outperform current state-of-the-art pause-only systems, and replicate to within 1% absolute the performance of our previously published hand-crafted baseline. The new system additionally offers scope for run-time control over the precision or recall of locations at which to speak.

  • 43. Laskowski, Kornel
    et al.
    Edlund, Jens
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Heldner, Mattias
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Learning prosodic sequences using the fundamental frequency variation spectrum2008In: Proceedings of the Speech Prosody 2008 Conference, Campinas, Brazil: Editora RG/CNPq , 2008, p. 151-154Conference paper (Refereed)
    Abstract [en]

    We investigate a recently introduced vector-valued representation of fundamental frequency variation, whose properties appear to be well-suited for statistical sequence modeling. We show what the representation looks like, and apply hidden Markov models to learn prosodic sequences characteristic of higher-level turn-taking phenomena. Our analysis shows that the models learn exactly those characteristics which have been reported for the phenomena in the literature. Further refinements to the representation lead to 12-17% relative improvement in speaker change prediction for conversational spoken dialogue systems.

  • 44. Laskowski, Kornel
    et al.
    Heldner, Mattias
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Edlund, Jens
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    The fundamental frequency variation spectrum2008In: Proceedings of FONETIK 2008, Gothenburg, Sweden: Department of Linguistics, University of Gothenburg , 2008, p. 29-32Conference paper (Other academic)
    Abstract [en]

    This paper describes a recently introduced vector-valued representation of fundamental frequency variation – the FFV spectrum – which has a number of desirable properties. In particular, it is instantaneous, continuous, distributed, and well suited for application of standard acoustic modeling techniques. We show what the representation looks like, and how it can be used to model prosodic sequences.

  • 45. Laukka, P.
    et al.
    Neiberg, Daniel
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Forsell, Mimmi
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Karlsson, Inger
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Elenius, Kjell
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Expression of Affect in Spontaneous Speech: Acoustic Correlates and Automatic Detection of Irritation and Resignation2011In: Computer speech & language (Print), ISSN 0885-2308, E-ISSN 1095-8363, Vol. 25, no 1, p. 84-104Article in journal (Refereed)
    Abstract [en]

    The majority of previous studies on vocal expression have been conducted on posed expressions. In contrast, we utilized a large corpus of authentic affective speech recorded from real-life voice controlled telephone services. Listeners rated a selection of 200 utterances from this corpus with regard to level of perceived irritation, resignation, neutrality, and emotion intensity. The selected utterances came from 64 different speakers who each provided both neutral and affective stimuli. All utterances were further automatically analyzed regarding a comprehensive set of acoustic measures related to F0, intensity, formants, voice source, and temporal characteristics of speech. Results first showed that several significant acoustic differences were found between utterances classified as neutral and utterances classified as irritated or resigned using a within-persons design. Second, listeners' ratings on each scale were associated with several acoustic measures. In general the acoustic correlates of irritation, resignation, and emotion intensity were similar to previous findings obtained with posed expressions, though the effect sizes were smaller for the authentic expressions. Third, automatic classification (using LDA classifiers both with and without speaker adaptation) of irritation, resignation, and neutral performed at a level comparable to human performance, though human listeners and machines did not necessarily classify individual utterances similarly. Fourth, clearly perceived exemplars of irritation and resignation were rare in our corpus. These findings were discussed in relation to future research.

  • 46. Massaro, D. W.
    et al.
    Cohen, M. M.
    Clark, R.
    Tabain, M.
    Beskow, Jonas
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Animated speech: Research progress and applications2012In: Audiovisual Speech Processing, Cambridge University Press, 2012, p. 309-345Chapter in book (Other academic)
    Abstract [en]

    Background This chapter is dedicated to Christian Benoît, who almost single-handedly established visible speech as an important domain of research and application. During and after his residence in our laboratory for the academic year 1991–92, Christian and his endearing partner Elisabeth were an important part of our lives. We shared in their marriage and the births of their two children, as well as in many professional challenges and puzzles. We hope that this book provides a legacy for Christian’s family and friends, and helps maintain a memory of his personal and professional value. The human face presents visual information during speech production that is critically important for effective communication. While the voice alone is usually adequate for communication (and can be turned into an engaging instrument by a skilled storyteller), visual information from movements of the lips, tongue, and jaws enhances intelligibility of the message (as is readily apparent with degraded auditory speech). For individuals with severe or profound hearing loss, understanding visible speech can make the difference between communicating effectively with others or a life of relative isolation. Moreover, speech communication is further enriched by the speaker’s facial expressions, emotions, and gestures (Massaro 1998b, Chapters 6, 7, 8).

  • 47.
    Neiberg, Daniel
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Ananthakrishnan, Gopal
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    On the Non-uniqueness of Acoustic-to-Articulatory Mapping2008In: Proceedings FONETIK 2008, Göteborg, 2008, p. 9-13Conference paper (Other academic)
    Abstract [en]

    This paper studies the hypothesis that the acoustic-to-articulatory mapping is non unique, statistically. The distributions of the acoustic and articulatory spaces are obtained by minimizing the BIC while fitting the data into a GMM using the EM algorithm. The kurtosisis used to measure the non-Gaussianity of the distributions and the Bhattacharya distance is used to find the difference between distributions of the acoustic vectors producing non unique articulator configurations. It is found that stop consonants and alveolar fricatives are generally not only non-linear but also non unique,while dental fricatives are found to be highly non-linear but fairly unique.

  • 48.
    Neiberg, Daniel
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    Ananthakrishnan, Gopal
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    Engwall, Olov
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    The Acoustic to Articulation Mapping: Non-linear or Non-unique?2008In: INTERSPEECH 2008: 9TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2008, BAIXAS: ISCA-INST SPEECH COMMUNICATION ASSOC , 2008, p. 1485-1488Conference paper (Refereed)
    Abstract [en]

    This paper studies the hypothesis that the acoustic-to-articulatory mapping is non-unique, statistically. The distributions of the acoustic and articulatory spaces are obtained by fitting the data into a Gaussian Mixture Model. The kurtosis is used to measure the non-Gaussianity of the distributions and the Bhattacharya distance is used to find the difference between distributions of the acoustic vectors producing non-unique articulator configurations. It is found that stop consonants and alveolar fricatives arc generally not only non-linear but also non-unique, while dental fricatives arc found to be highly non-linear but fairly unique. Two more investigations are also discussed: the first is on how well the best possible piecewise linear regression is likely to perform, the second is on whether the dynamic constraints improve the ability to predict different articulatory regions corresponding to the same region in the acoustic space.

  • 49.
    Neiberg, Daniel
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    Elenius, Kjell
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT. KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
    Automatic Recognition of Anger in Spontaneous Speech2008In: INTERSPEECH 2008: 9TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2008, BAIXAS: ISCA-INST SPEECH COMMUNICATION ASSOC , 2008, p. 2755-2758Conference paper (Refereed)
    Abstract [en]

    Automatic detection of real life negative emotions in speech has been evaluated using Linear Discriminant Analysis, LDA, with "classic" emotion features and a classifier based on Gaussian Mixture Models, GMMs. The latter uses Mel-Frequency Cepstral Coefficients, MFCCs, from a filter bank covering the 300-3400 Hz region to capture spectral shape and formants, and another in the 20-600 Hz region to capture prosody. Both classifiers have been tested on an extensive corpus from Swedish voice controlled telephone services. The results indicate that it is possible to detect anger with reasonable accuracy (average recall 83%) in natural speech and that the GMM method performed better than the LDA one.

  • 50.
    Neiberg, Daniel
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    Gustafson, Joakim
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Speech Technology, CTT.
    The Prosody of Swedish Conversational Grunts2010In: 11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010, 2010, p. 2562-2565Conference paper (Refereed)
    Abstract [en]

    This paper explores conversational grunts in a face-to-face setting. The study investigates the prosody and turn-taking effect of fillers and feedback tokens that has been annotated for attitudes. The grunts were selected from the DEAL corpus and automatically annotated for their turn taking effect. A novel suprasegmental prosodic signal representation and contextual timing features are used for classification and visualization. Classification results using linear discriminant analysis, show that turn-initial feedback tokens lose some of their attitude-signaling prosodic cues compared to non-overlapping continuer feedback tokens. Turn taking effects can be predicted well over chance level, except Simultaneous Starts. However, feedback tokens before places where both speakers take the turn were more similar to feedback continuers than to turn initial feedback tokens.

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