Speaker compensation with sine-log all-pass transforms

In previous work, we proposed the rational all-pass transform (RAPT) as the basis of a speaker adaptation scheme intended for use with a large vocabulary speech recognition system. It was shown that RAPT-based adaptation reduces to a linear transformation of cepstral means, much like the better known maximum likelihood linear regression (MLLR). In a set of speech recognition experiments conducted on the Switchboard Corpus, we obtained a word error rate (WER) of 37.9% using RAPT adaptation, a significant improvement over the 39.5% WER achieved with MLLR. In the present work, we propose the sine-log all-pass transform (SLAPT) as a replacement for the RAPT. Our findings indicate the SLAPT is just as effective as the RAPT at reducing WER when used as the basis for a variety of speaker compensation schemes, but in addition conduces to far more tractable computation of transformed cepstral sequences, and the estimation of optimal transform parameters