This paper proposes a new speaker adaptation method using speaker weights for multiple reference speaker training. The speaker weights are calculated to reflect the similarity of each reference speaker's dynamic features to an input speaker. They are used to have the similarities affect to hidden Markov models. The evaluation experiments are carried out through the /b,d,g,m,n,N/ phoneme recognition task using 8 speakers. Average recognition rates are 68.0%, 66.4%, and 65.6% respectively for three test sets which have different speech styles, that is, word utterances, phrase-by-phrase utterances and continuous utterances. These are 1.6%, 6.7%, and 8.2% respectively higher than the supplemented HMM rates.
@inproceedings{hattori90_icslp, title = {Speaker weighted training of HMM using multiple reference speakers}, author = {Hiroaki Hattori and Satoshi Nakamura and Kiyohiro Shikano and Shigeki Sagayama}, year = {1990}, booktitle = {First International Conference on Spoken Language Processing (ICSLP 1990)}, pages = {149--152}, doi = {10.21437/ICSLP.1990-38}, issn = {2958-1796},}Cite as:Hattori, H., Nakamura, S., Shikano, K., Sagayama, S. (1990) Speaker weighted training of HMM using multiple reference speakers. Proc. First International Conference on Spoken Language Processing (ICSLP 1990), 149-152, doi: 10.21437/ICSLP.1990-38