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Auditory model based optimization of MFCCs improves automatic speech recognition performance
KTH, School of Electrical Engineering (EES), Sound and Image Processing.ORCID iD: 0000-0003-2638-6047
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
2009 (English)In: INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, 2009, 2943-2946 p.Conference paper, Published paper (Refereed)
Abstract [en]

Using a spectral auditory model along with perturbation based analysis, we develop a new framework to optimize a set of features such that it emulates the behavior of the human auditory system. The optimization is carried out in an off-line manner based on the conjecture that the local geometries of the feature domain and the perceptual auditory domain should be similar. Using this principle, we modify and optimize the static mel frequency cepstral coefficients (MFCCs) without considering any feedback from the speech recognition system. We show that improved recognition performance is obtained for any environmental condition, clean as well as noisy.

Place, publisher, year, edition, pages
2009. 2943-2946 p.
Keyword [en]
ASR, Auditory model, MFCC
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-11468ISI: 000276842801277Scopus ID: 2-s2.0-70450221097ISBN: 978-1-61567-692-7 (print)OAI: oai:DiVA.org:kth-11468DiVA: diva2:276951
Conference
10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009; Brighton; 6 September 2009 - 10 September 2009
Note
QC 20101015Available from: 2009-11-13 Created: 2009-11-13 Last updated: 2012-09-14Bibliographically approved
In thesis
1. A study on selecting and optimizing perceptually relevant features for automatic speech recognition
Open this publication in new window or tab >>A study on selecting and optimizing perceptually relevant features for automatic speech recognition
2009 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The performance of an automatic speech recognition (ASR) 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. This work is motivated by the fact that humans perform better at speech recognition than machines, particularly for noisy environments. The goal of this thesis is to make use of knowledge of human perception in the selection and optimization of speech features for speech recognition.

Papers A and C show that robust feature selection for speech recognition can be based on models of the human auditory system. These papers show that maximizing the similarity of the Euclidian geometry of the features to the geometry of the perceptual domain is a powerful tool to select features. Whereas conventional methods optimize classification performance, the new feature selection method exploits knowledge implicit in the human auditory system, inheriting its robustness to varying environmental conditions. The proposed algorithm show how the feature set can be learned from perception only by establishing a measure of goodness for a given feature based on a perturbation analysis and distortion criteria derived from psycho-acoustic models. Experiments with a practical speech recognizer confirm the validity of the principle.

 In Paper B the perceptually relevant objective criterion is used to define new features. Again the motivation has its origin at the human peripheral auditory system which plays a major role to the input speech signal until it reaches the central auditory system of the brain where the recognition occurs. While many feature extraction techniques incorporate knowledge of the auditory system, the procedures are usually designed for a specific task, and they lack of the most recently gained knowledge on human hearing. Paper B shows an approach to improve mel frequency cepstrum coefficients (MFCCs) through off-line optimization. The method has three advantages: i) it is computational 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

 

Place, publisher, year, edition, pages
Stockholm: KTH, 2009. xv, 37 p.
Series
Trita-EE, ISSN 1653-5146 ; 2009:049
Identifiers
urn:nbn:se:kth:diva-11470 (URN)978-91-7415-478-8 (ISBN)
Presentation
2009-11-27, E2, KTH, Lindstedtsvägen 3, Stockholm, 10:00 (English)
Opponent
Supervisors
Available from: 2009-11-13 Created: 2009-11-13 Last updated: 2010-10-15Bibliographically approved
2. 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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