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Vocal tract length compensation in the signal and model domains in child speech recognition
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. (TAL)
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH.
2007 (English)In: Proceedings of Fonetik: TMH-QPSR, 2007, 41-44 p.Conference paper (Other academic)
Abstract [en]

In a newly started project, KOBRA, we study methods to reduce the required amount of training data for speech recognition by combining the conventional data-driven training approach with available partial knowledge on speech production, implemented as transformation functions in the acoustic, articulatory and speaker characteristic domains. Initially, we investigate one well-known dependence, the inverse proportional relation between vocal tract length and formant frequencies. In this report, we have replaced the conventional technique of frequency warping the unknown input utterance (VTLN) by transforming the training data instead. This enables phoneme-dependent warping to be performed. In another experiment, we expanded the available training data by duplicating each training utterance into a number of differently warped instances. Training on this expanded corpus results in models, each one representing the whole range of vocal tract length variation. This technique allows every frame of the utterance to be warped differently. The computational load is reduced by an order of magnitude compared to conventional VTLN without notice- able decrease in performance on the task of recognising children’s speech using models trained on adult speech.

Place, publisher, year, edition, pages
2007. 41-44 p.
National Category
Language Technology (Computational Linguistics)
URN: urn:nbn:se:kth:diva-12333OAI: diva2:309765
Proceedings of Fonetik, 2007, 30 maj - 1 juni Sal F2, Lindstedtsvägen 26, KTH, Stockholm

QC 20110502

Available from: 2010-04-09 Created: 2010-04-08 Last updated: 2016-05-23Bibliographically approved
In thesis
1. Accounting for Individual Speaker Properties in Automatic Speech Recognition
Open this publication in new window or tab >>Accounting for Individual Speaker Properties in Automatic Speech Recognition
2010 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

In this work, speaker characteristic modeling has been applied in the fields of automatic speech recognition (ASR) and automatic speaker verification (ASV). In ASR, a key problem is that acoustic mismatch between training and test conditions degrade classification per- formance. In this work, a child exemplifies a speaker not represented in training data and methods to reduce the spectral mismatch are devised and evaluated. To reduce the acoustic mismatch, predictive modeling based on spectral speech transformation is applied. Follow- ing this approach, a model suitable for a target speaker, not well represented in the training data, is estimated and synthesized by applying vocal tract predictive modeling (VTPM). In this thesis, the traditional static modeling on the utterance level is extended to dynamic modeling. This is accomplished by operating also on sub-utterance units, such as phonemes, phone-realizations, sub-phone realizations and sound frames.

Initial experiments shows that adaptation of an acoustic model trained on adult speech significantly reduced the word error rate of ASR for children, but not to the level of a model trained on children’s speech. Multi-speaker-group training provided an acoustic model that performed recognition for both adults and children within the same model at almost the same accuracy as speaker-group dedicated models, with no added model complexity. In the analysis of the cause of errors, body height of the child was shown to be correlated to word error rate.

A further result is that the computationally demanding iterative recognition process in standard VTLN can be replaced by synthetically extending the vocal tract length distribution in the training data. A multi-warp model is trained on the extended data and recognition is performed in a single pass. The accuracy is similar to that of the standard technique.

A concluding experiment in ASR shows that the word error rate can be reduced by ex- tending a static vocal tract length compensation parameter into a temporal parameter track. A key component to reach this improvement was provided by a novel joint two-level opti- mization process. In the process, the track was determined as a composition of a static and a dynamic component, which were simultaneously optimized on the utterance and sub- utterance level respectively. This had the principal advantage of limiting the modulation am- plitude of the track to what is realistic for an individual speaker. The recognition error rate was reduced by 10% relative compared with that of a standard utterance-specific estimation technique.

The techniques devised and evaluated can also be applied to other speaker characteristic properties, which exhibit a dynamic nature.

An excursion into ASV led to the proposal of a statistical speaker population model. The model represents an alternative approach for determining the reject/accept threshold in an ASV system instead of the commonly used direct estimation on a set of client and impos- tor utterances. This is especially valuable in applications where a low false reject or false ac- cept rate is required. In these cases, the number of errors is often too few to estimate a reli- able threshold using the direct method. The results are encouraging but need to be verified on a larger database.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2010. xiv, 43 p.
Trita-CSC-A, ISSN 1653-5723 ; 2010:05
MAP, MLLR, VTLN, speaker characteristics, dynamic modeling, child
National Category
Language Technology (Computational Linguistics)
urn:nbn:se:kth:diva-12258 (URN)978-91-7415-605-8 (ISBN)
2010-04-23, Fantum, KTH, Lindstedtsvägen 24, SE-100 44 STOCKHOLM, SWEDEN, 15:15 (English)
QC 20110502Available from: 2010-04-08 Created: 2010-03-30 Last updated: 2011-05-02Bibliographically approved

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