A codebook adaptation algorithm for SCHMM using formant distribution

This paper describes a codebook adaptation process improving the performance of speaker adaptation. The proposed method is performed prior to Bayesian speaker adaptation method using the formant distribution of adaptation data. The reference codebook is adapted to represent the formant distribution of a new speaker. The average recognition rate of Bayesian adaptation is improved from 91.4% to 95.1% using the proposed method. The proposed method is e ective particularly when there exists a large mismatch between the reference codebook and a target speaker in feature space. In this cases the average recognition rate is 95.0% while 89.9% is obtained when only Bayesian adaptation is performed.