Performance prediction of speech recognition using average-voice-based speech synthesis

This paper describes a performance prediction technique of a speech recognition system using a small amount of target speakers' data. In the conventional HMM-based technique, a speaker-dependent model was used and thus a considerable amount of training data was needed. To reduce the amount of training data, we introduce an average voice model as a prior knowledge for the target speakers' acoustic models, and adapt it to the target speakers' ones using speaker adaptation. Experimental results show that the use of average voice model effectively save the amount of training data of the target speakers, and the prediction accuracy is significantly improved compared to the conventional technique especially when a smaller amount of training data is available.