Combining EigenVoices and Structural MLLR for Speaker Adaptation

Fabrice Lauri 1 Irina Illina 1 Dominique Fohr 1
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : This papers considers the problem of speaker adaptation of acoustic models in speech recognition. We have investigated four possible methods which integrate the concepts of both Structural Maximum Likelihood Linear regression (SMLLR) and EigenVoices-based technique (EV) to adapt the Gaussian means of the speaker independant models for a new speaker. The experiments were evaluated using the speech recognition engine ESPERE on the data of the corpus Resource Management. They show that all of the proposed methods can improve the performances of an automatic speech recognition system (ASRS) in supervised batch adaptation as efficiently as SMLLR and EigenVoices-based techniques whatever the amount of adaptation data is available. For an unsupervised incremental adaptation, only the approach SMLLR+SEV gives the best results.
Type de document :
Communication dans un congrès
IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP'03, Apr 2003, Hong Kong, China, 4 p, 2003
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https://hal.inria.fr/inria-00099746
Contributeur : Publications Loria <>
Soumis le : mardi 26 septembre 2006 - 09:40:54
Dernière modification le : jeudi 11 janvier 2018 - 06:19:57

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  • HAL Id : inria-00099746, version 1

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Fabrice Lauri, Irina Illina, Dominique Fohr. Combining EigenVoices and Structural MLLR for Speaker Adaptation. IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP'03, Apr 2003, Hong Kong, China, 4 p, 2003. 〈inria-00099746〉

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