Speaker adaptation based on transfer vector field smoothing using maximum a posteriori probability estimation

The paper proposes a novel speech adaptation algorithm that enables adaptation even with a small amount of speech data. This is a unified algorithm of two efficient conventional speaker adaptation techniques, which are maximum a posteriori (MAP) estimation and transfer vector field smoothing (VFS). This algorithm is designed to avoid the weaknesses of both MAP and VFS. A higher phoneme recognition performance was obtained by using this algorithm than with individual methods, showing the superiority of the proposed algorithm. The phoneme recognition error rate was reduced from 22.0% to 19.1% using this algorithm for a speaker-independent model with seven adaptation phrases. Furthermore, a priori knowledge concerning speaker characteristics was obtained for this algorithm by generating an initial HMM with the speech of a selected speaker cluster based on speaker similarity. The adaptation using this initial model reduced the phoneme recognition error rate from 22.0% to 17.7%