Vector quantization for speaker adaptation results on a 5000 word database

With a view to designing a speaker-independent large vocabulary recognition system, we evaluate a vector quantization approach for speaker adaptation. Only one speaker (the reference speaker) pronounces the application vocabulary. He also pronounces a small vocabulary called the adaptation vocabulary. Each new speaker then merely pronounces the adaptation vocabulary. We have compared two adaptation methods, establishing a correspondence between the codebooks of the reference and the new speakers, on a 20-speaker database with a 104-word application vocabulary. Method I uses a transposed codebook to represent the new speaker during the recognition process, whereas Method II uses a codebook which is obtained by clustering analysis on the NS's pronunciation of the adaptation vocabulary. The adaptation vocabulary contains 136 words. Comparison of the performance of the two methods shows that a new speaker's codebook is not necessary to represent the new speaker. Consequently we have used the first method to perform tests with a 5000-word application vocabulary, and a 4-speaker database. The adaptation is still efficient (the mean improvement is about 14{\%}), even if the relative improvement is 30{\%} compared to 56{\%} obtained in the 104-word application experiment. Further experiments show that the recognition accuracy can be improved by increasing the adaptation vocabulary size and the codebook size. {\textcopyright} 1991.