An Automatic Retraining Method for Speaker Independent Hidden Markov Models

Abstract. When training speaker-independent HMM-based acoustic models, a lot of manually transcribed acoustic training data must be available from a good many different speakers. These training databases have a great variation in the pitch of the speakers, articulation and the speed of talking. In practice, the speaker-independent models are used for bootstrapping the speaker-dependent models built by speaker adaptation methods. Thus the performance of the adapta-tion methods is strongly influenced by the performance of the speaker-independent model and by the accuracy of the automatic segmentation which also depends on the base model. In practice, the performance of the speaker-independent models can vary a great deal on the test speakers. Here our goal is to reduce this performance variability by increasing the performance value for the speakers with low values, at the price of allowing a small drop in the highest performance values. For this purpose we propose a new method for the automatic retraining of speaker-independent HMMs.