Model-based speaker normalization methods for speech recognition

A speaker normalization method using a speech generation model is proposed in order to achieve high-performance speaker adaptation with a small amount of adaptation data. The speaker-and phoneme-dependent vocal tract area function is approximated by the corresponding area function produced by the articulatory model of a standard speaker, combined with phoneme-independent feature quantities of the vocal-tract shape of the normalized target speaker as estimated from the formant frequencies of two vowels. The frequency warping functions are determined from the formant frequencies of speech calculated from the vocal-tract area functions thus obtained, and normalization of the uttered speech is performed by stretching the speech spectrum in the frequency-axis direction. Continuous phoneme recognition experiments using phoneme connection rules show that the recognition error using a gender-dependent model is reduced by about 30% in the proposed method and that recognition performance superior to that of vocal-tract length normalization is obtained. The recognition performance of the proposed method is also equivalent to that of speaker adaptation by moving vector field smoothing (VFS) using 10 phonetically balanced sentences, showing that high-performance speaker adaptation using a small amount of adaptation data can be achieved by the proposed method.