A structural Bayes approach to speaker adaptation Export

@article{citeulike:1614953, abstract = {Maximum a posteriori (MAP) estimation has been successfully applied to speaker adaptation in speech recognition systems using hidden Markov models. When the amount of data is sufficiently large, MAP estimation yields recognition performance as good as that obtained using maximum-likelihood (ML) estimation. This paper describes a structural maximum a posteriori (SMAP) approach to improve the MAP estimates obtained when the amount of adaptation data is small. A hierarchical structure in the model parameter space is assumed and the probability density functions for model parameters at one level are used as priors for those of the parameters at adjacent levels. Results of supervised adaptation experiments using nonnative speakers' utterances showed that SMAP estimation reduced error rates by 61\% when ten utterances were used for adaptation and that it yielded the same accuracy as MAP and ML estimation when the amount of data was sufficiently large. Furthermore, the recognition results obtained in unsupervised adaptation experiments showed that SMAP estimation was effective even when only one utterance from a new speaker was used for adaptation. An effective way to combine rapid supervised adaptation and on-line unsupervised adaptation was also investigated}, author = {Shinoda, K. and Lee, C. H.}, booktitle = {Speech and Audio Processing, IEEE Transactions on}, citeulike-article-id = {1614953}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=906001}, journal = {Speech and Audio Processing, IEEE Transactions on}, keywords = {centroid, kullback-leibler}, number = {3}, pages = {276--287}, posted-at = {2007-09-03 03:32:17}, priority = {2}, title = {A structural Bayes approach to speaker adaptation}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=906001}, volume = {9}, year = {2001} }

TY - JOUR ID - citeulike:1614953 L3 - citeulike-article-id:1614953 N2 - Maximum a posteriori (MAP) estimation has been successfully applied to speaker adaptation in speech recognition systems using hidden Markov models. When the amount of data is sufficiently large, MAP estimation yields recognition performance as good as that obtained using maximum-likelihood (ML) estimation. This paper describes a structural maximum a posteriori (SMAP) approach to improve the MAP estimates obtained when the amount of adaptation data is small. A hierarchical structure in the model parameter space is assumed and the probability density functions for model parameters at one level are used as priors for those of the parameters at adjacent levels. Results of supervised adaptation experiments using nonnative speakers' utterances showed that SMAP estimation reduced error rates by 61% when ten utterances were used for adaptation and that it yielded the same accuracy as MAP and ML estimation when the amount of data was sufficiently large. Furthermore, the recognition results obtained in unsupervised adaptation experiments showed that SMAP estimation was effective even when only one utterance from a new speaker was used for adaptation. An effective way to combine rapid supervised adaptation and on-line unsupervised adaptation was also investigated IS - 3 JF - Speech and Audio Processing, IEEE Transactions on EP - 287 TI - A structural Bayes approach to speaker adaptation VL - 9 T2 - Speech and Audio Processing, IEEE Transactions on SP - 276 KW - centroid KW - kullback-leibler AU - Shinoda, K AU - Lee, CH PY - 2001/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=906001 ER -