Speaker adaptation is an efficient way to model a new speaker from an existing speaker-independent model with limited speakerdependent data. In this paper, we investigate the use of discriminative training schemes based on the minimum phone error (MPE) criterion to improve a well-known speaker adaptation technique, a combination of transform-based adaptation and Bayesian adaptation. Furthermore, a new approach utilizing the statistics of the model-based regression tree for controlling the interpolation between maximum likelihood (ML) and MPE objective functions is also presented. Several comparative experiments were conducted on a continuous speech recognition task for Mandarin Chinese. Experimental results show that the proposed approach can further improve the performance of the original hybrid adaptation.
Bibliographic reference. Chen, Liang-Yu / Lee, Chun-Jen / Jang, Jyh-Shing Roger (2008): "Minimum phone error discriminative training for Mandarin Chinese speaker adaptation", In INTERSPEECH-2008, 1241-1244.