Fast speaker adaptation: some experiments on different techniques for codebook and HMM parameters estimation

A set of techniques to perform fast speaker adaptation for a large vocabulary, natural-language, speech recognition system are presented. The experimentation has been carried out using a 20000-word, real-time, natural-language speech recognizer for the Italian language. To perform speaker adaptation within the framework of the probabilistic approach to speech recognition two different problems must be addressed: codebook adaptation and hidden Markov model parameters adaptation. The basic idea is to use a set of data collected from several different speakers as a source of a priori knowledge with a small speech sample provided by the new speaker to perform the adaptation task. Several different techniques for codebook adaptation have been tried and discussed.<>