Maximum likelihood linear regression adaptation for the polynomial segment models

Speaker adaptation has long been applied to improve speech recognition performance of hidden Markov model (HMM)-based systems. Recently, the polynomial segment model (PSM) has been shown as a viable alternative that can significantly improve the performance of large vocabulary continuous speech recognition (LVCSR). In this letter, we extend the widely used HMM-based maximum likelihood linear regression (MLLR) speaker adaptation technique to PSMs. PSM properties, such as using segment as a modeling unit, and a polynomial curve as model mean, are taken into account in deriving the PSM-based MLLR. Experiments show that PSM-based MLLR adaptation performs equally well as the HMM-based MLLR adaptation with about 19% relative improvement from the SI model. In addition, another 5% relative improvement can be obtained by combining the adapted PSMs and HMMs.