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Auditory and Dynamic Modeling Paradigms to Detect L2 Mispronunciations
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. (Centre for Speech Technology)
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. (Centre for Speech Technology)ORCID iD: 0000-0003-4532-014X
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. (Centre for Speech Technology)ORCID iD: 0000-0002-3323-5311
2012 (English)In: 13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012, Vol 1, 2012, 898-901 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper expands our previous work on automatic pronunciation error detection that exploits knowledge from psychoacoustic auditory models. The new system has two additional important features, i.e., auditory and acoustic processing of the temporal cues of the speech signal, and classification feedback from a trained linear dynamic model. We also perform a pronunciation analysis by considering the task as a classification problem. Finally, we evaluate the proposed methods conducting a listening test on the same speech material and compare the judgment of the listeners and the methods. The automatic analysis based on spectro-temporal cues is shown to have the best agreement with the human evaluation, particularly with that of language teachers, and with previous plenary linguistic studies.

Place, publisher, year, edition, pages
2012. 898-901 p.
Keyword [en]
L2 pronunciation error, auditory model, linear dynamic model, distortion measure, phoneme
National Category
Signal Processing Other Computer and Information Science Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-102317ISI: 000320827200225Scopus ID: 2-s2.0-84878407679ISBN: 978-1-62276-759-5 (print)OAI: oai:DiVA.org:kth-102317DiVA: diva2:552320
Conference
13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012; Portland, OR; United States; 9 September 2012 through 13 September 2012
Note

QC 20120914

Available from: 2012-09-13 Created: 2012-09-13 Last updated: 2013-08-22Bibliographically approved
In thesis
1. Perceptually motivated speech recognition and mispronunciation detection
Open this publication in new window or tab >>Perceptually motivated speech recognition and mispronunciation detection
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This doctoral thesis is the result of a research effort performed in two fields of speech technology, i.e., speech recognition and mispronunciation detection. Although the two areas are clearly distinguishable, the proposed approaches share a common hypothesis based on psychoacoustic processing of speech signals. The conjecture implies that the human auditory periphery provides a relatively good separation of different sound classes. Hence, it is possible to use recent findings from psychoacoustic perception together with mathematical and computational tools to model the auditory sensitivities to small speech signal changes.

The performance of an automatic speech recognition system strongly depends on the representation used for the front-end. If the extracted features do not include all relevant information, the performance of the classification stage is inherently suboptimal. The work described in Papers A, B and C is motivated by the fact that humans perform better at speech recognition than machines, particularly for noisy environments. The goal is to make use of knowledge of human perception in the selection and optimization of speech features for speech recognition. These papers show that maximizing the similarity of the Euclidean geometry of the features to the geometry of the perceptual domain is a powerful tool to select or optimize features. Experiments with a practical speech recognizer confirm the validity of the principle. It is also shown an approach to improve mel frequency cepstrum coefficients (MFCCs) through offline optimization. The method has three advantages: i) it is computationally inexpensive, ii) it does not use the auditory model directly, thus avoiding its computational cost, and iii) importantly, it provides better recognition performance than traditional MFCCs for both clean and noisy conditions.

The second task concerns automatic pronunciation error detection. The research, described in Papers D, E and F, is motivated by the observation that almost all native speakers perceive, relatively easily, the acoustic characteristics of their own language when it is produced by speakers of the language. Small variations within a phoneme category, sometimes different for various phonemes, do not change significantly the perception of the language’s own sounds. Several methods are introduced based on similarity measures of the Euclidean space spanned by the acoustic representations of the speech signal and the Euclidean space spanned by an auditory model output, to identify the problematic phonemes for a given speaker. The methods are tested for groups of speakers from different languages and evaluated according to a theoretical linguistic study showing that they can capture many of the problematic phonemes that speakers from each language mispronounce. Finally, a listening test on the same dataset verifies the validity of these methods.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2012. xxi, 79 p.
Series
Trita CSC-A, ISSN 1653-5723 ; 2012:10
Keyword
feature extraction, feature selection, auditory models, MFCCs, speech recognition, distortion measures, perturbation analysis, psychoacoustics, human perception, sensitivity matrix, pronunciation error detection, phoneme, second language, perceptual assessment
National Category
Computer Science Signal Processing Media and Communication Technology Other Computer and Information Science
Identifiers
urn:nbn:se:kth:diva-102321 (URN)978-91-7501-468-5 (ISBN)
Public defence
2012-10-05, A2, Östermalmsgatan 26, KTH, Stockholm, 10:00 (English)
Opponent
Supervisors
Projects
European Union FP6-034362 research project ACORNSComputer-Animated language Teachers (CALATea)
Note

QC 20120914

Available from: 2012-09-14 Created: 2012-09-13 Last updated: 2012-09-14Bibliographically approved

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Salvi, Giampiero

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  • apa
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