Combining EigenVoices and structural MLLR for speaker adaptation

This paper considers the problem of speaker adaptation of acoustic models in speech recognition. We have investigated four different 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 independent 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.