This paper describes a new supervised speaker adaptation method based on vector field smoothing, for small size adaptation data. This method assumes that the correspondence of feature vectors between speakers can be viewed as a kind of smooth vector field, and interpolation and smoothing of the correspondence are introduced into the adaptation process for higher adaptation performance.

The proposed adaptation method was applied to discrete HMM based speech recognition. The evaluation experiments showed that the proposed method with 10 word adaptation data, produced almost the same results as the conventional codebook mapping method with 25 words. These experiments clearly confirmed the effectiveness of the proposed method.