  We present SVCnet, a system for modelling speaker variability. 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 an MS-TDNN recognizer is described
