Improved Bayesian learning of hidden Markov models for speaker adaptation

Jen-Tzung Chien*, Chin Hui Lee, Hsiao Chuan Wang

*此作品的通信作者

    研究成果: Conference article同行評審

    18 引文 斯高帕斯(Scopus)

    摘要

    We propose an improved maximum a posteriori (MAP) learning algorithm of continuous-density hidden Markov model (CDHMM) parameters for speaker adaptation. The algorithm is developed by sequentially combining three adaptation approaches. First, the clusters of speaker-independent HMM parameters are locally transformed through a group of transformation functions. Then, the transformed HMM parameters are globally smoothed via the MAP adaptation. Within the MAP adaptation, the parameters of unseen units in adaptation data are further adapted by employing the transfer vector interpolation scheme. Experiments show that the combined algorithm converges rapidly and outperforms those other adaptation methods.

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