Speaker Weighted Training of HMM Using Multiple Reference Speakers

概要

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This paper proposes a new speaker adaptation method using a speaker weighting technique for multiple reference speaker training of a hidden Markov model (HMM). The proposed method considers the similarities between an input speaker and multiple reference speakers, and use the similarities to control the influence of the reference speakers upon HMM. The evaluation experiments were carried out through the / b,d,g,m,n,N / phoneme recognition task using 8 speakers. Average recognition rates were 68.0%, 66.4%, and 65.6% respectively for three test sets which have different speech styles. These were .8%, 8.8%, and 10.5% higher than the rates of the spectrum mapping method, and also 1.6%, 6.7%, and 8.2% higher than the rates of the multiple reference speaker training, the supplemented HMM. The evaluation experiments clarified the effectiveness of the proposed method.
社団法人電子情報通信学会の論文
1993-02-25