Performance comparisons of all-pass transform adaptation with maximum likelihood linear regression

All-pass transform (APT) adaptation transforms the cepstral means of a hidden Markov model so as to mimic the effect of warping the short-time frequency axis of a segment of speech, much like vocal tract length normalization (VTLN). However, APT adaptation can be implemented as a linear transformation in the cepstral domain, much like the better known maximum likelihood linear regression (MLLR). Recent work demonstrated the superior performance of APT adaptation to MLLR for a large vocabulary conversational speech recognition task. This work presents similar comparisons on the switchboard corpus. We found that without VTLN, the best MLLR and APT systems achieved word error rates (WERs) of 43.0% and 40.2% respectively. Similarly, with VTLN the respective error rates were 40.3%, and 39.2%, so that APT adaptation is significantly better in both cases. We also undertook a set of experiments to determine whether APT adaptation can be combined with a linear semi-tied covariance (STC) transform. With a single APT per speaker, the application of STC reduced the WER from 42.9% to 39.4%.