Supplementation of HMM for articulatory variation in speaker adaptation

A method of dealing with articulatory speaker variations in hidden Markov models (HMMs) for speaker adaptation is proposed. Speech data from many speakers are spectrally mapped onto a standard speaker. These data are used to teach the HMM the interspeaker articulatory variations that subsist across the spectral mapping. The proposed method is compared to other adaptation methods through the /b,d,g/ recognition task. The results show 82.5% recognition accuracy, which is better than the rates of other methods. Evaluation experiments on a Japanese all phoneme recognition task and a continuous-speech recognition task are reported. Average recognition rates for Japanese all phonemes are 71.3% and 93.2%, for the best candidate and the top-three candidates, respectively. These are 0.7% and 1.5% higher than the rates of the basic spectrum mapping method. In the continuous-speech recognition experiment, average phrase recognition rates are 74.9% and 96.2%, for the best candidate and the top-five candidates, respectively