Speaker Clustering and Transformation Forspeaker Adaptation in Large - Vocabulary

1 ABSTRACT A speaker adaptation strategy is described that is based on nding a subset of speakers, from the training set, who are acoustically close to the test speaker, and using only the data from these speakers (rather than the complete training corpus) to re-estimate the system parameters. Further, a linear transformation is computed for every one of the selected training speakers to better map the training speaker's data to the test speaker's acoustic space. Finally, the system parameters (Gaussian means) are re-estimated speciically for the test speaker using the transformed data from the selected training speakers. Experiments showed that this scheme is capable of reducing the error rate by 10-15% with the use of as little as 3 sentences of adaptation data. 2 INTRODUCTION In the last couple of years, several advances have been made in improving the error rate of continuous-speech-recognition systems 1]. For instance, typical word-error rates on test data drawn from the Wall Street Journal database, as reported by diierent participants in the Wall Street Journal task 1], hover in the neighborhood of 12 %, for large-vocabulary speaker-independent systems. Though this represents a reasonable level of performance on this particular test data, there is still scope for further improvement. One way to improve the performance of these systems is to make the system parameters speaker-dependent. However, large-vocabulary systems tend to have a large number of parameters, and in order to robustly estimate these parameters, a large amount of training data is needed. This implies that the test speaker will have to furnish a large amount of data to speciically train the system to his/her speech. This is usually not a practical solution. Consequently, most systems use speaker adaptation techniques, that require only a small amount of data from the test speaker. This data is used to move the parameters of the speaker-independent system towards speaker-dependent values. In this paper, we present a speaker adaptation method that is based on nding a cluster of speakers who are acoustically 'close' to the test speaker, and using these speakers to estimate model parameters, that are closer to the test speaker's data than the speaker-independent model parameters. Further, this method assumes that the basic speech recognition system uses HMM's to model the speech production process, and mixtures of continuous-density Gaussian pdf's to model the output distribution of the HMM's. 3 TECHNICAL BACKGROUND Some adaptation schemes that have been proposed recently …