On the incremental addition of regression classes for speaker adaptation
J. McDonough, V. Venkatoramani, Bill Byrne · 2002
We previously proposed the all-pass transform (APT) as the basis of a speaker adaptation scheme intended for use with a large vocabulary speech recognition system. It was shown that APT-based adaptation reduces to a linear transformation of cepstral means, much like the better known maximum likelihood linear regression (MLLR). Due to this linearity, APT-based adaptation can be used in conjunction with speaker-adapted training (SAT), an algorithm for performing maximum likelihood estimation of the parameters of a hidden Markov model when speaker adaptation is to be employed during both training and test. In other work, we proposed a refinement of SAT dubbed single-pass adapted training (SPAT) specifically-tailored for use with the APT. Here we introduce an incremental training procedure intended for use with the APT and multiple regression classes. In a set of speech recognition experiments conducted on the Switchboard Corpus, we obtained a word error rate of 37.9% using APT adaptation, a significant improvement over the 39.5% word error rate achieved with MLLR.