Speaker adaptation from limited training in the BBN BYBLOS Speech Recognition system
- 1 January 1989
- proceedings article
- Published by Association for Computational Linguistics (ACL)
- p. 100-105
- https://doi.org/10.3115/100964.100969
Abstract
The BBN BYBLOS continuous speech recognition system has been used to develop a method of speaker adaptation from limited training. The key step in the method is the estimation of a probabilistic spectral mapping between a prototype speaker, for whom there exists a well-trained speaker-dependent hidden Markov model (HMM), and a target speaker for whom there is only a small amount of training speech available. The mapping defines a set of transformation matrices which are used to modify the parameters of the prototype model. The resulting transformed model is then used as an approximation to a well-trained model for the target speaker. We review the techniques employed to accomplish this transformation and present experimental results conducted on the DARPA Resource Management database.This publication has 0 references indexed in Scilit: