A technique for using the speech of multiple reference speakers as a basis for speaker adaptation in large-vocabulary continuous-speech recognition is introduced. In contrast to other methods that use a pooled reference model, this technique normalizes the training speech from multiple reference speakers to a single common feature space before pooling it. The normalized and pooled speech is then treated as if it came from a single reference speaker for training the reference hidden Markov model (HMM). The usual probabilistic spectrum transformation can be applied to the reference HMM to model a new speaker. Preliminary experimental results are reported from applying this approach to over 100 reference speakers from the speaker-independent portion of the DARPA 1000-Word Resource Management Database.
展开▼
