Rapid connectionist speaker adaptation

SVCnet, a system for modeling speaker variability, is presented. Encoder neural networks specialized for each speech sound produce low-dimensionality models of acoustical variation, and these models are further combined into an overall model of voice variability. A training procedure is described which minimizes the dependence of this model on which sounds have been uttered. Using the trained model (SVCnet) and a brief, unconstrained sample of a new speaker´s voice, the system produces a speaker voice code that can be used to adapt a recognition system to the new speaker without retraining. A system which combines SVCnet with a MS-TDNN recognizer is described