Feature Space VTS with Phase Term Modeling

摘要

A new variant of Vector Taylor Series based features compensation algorithm is proposed. The phase-sensitive speech distortion model is used and the phase term is modeled as a multivariate gaussian with unknown mean vector and covariance matrix. These parameters are estimated based on Maximum Likelihood principle and EM-algorithm is used for this. EM formulas of parameter update are derived as well MMSE estimate of the clean speech features. The experiments on Aurora2 database show that taking phase term into account and data-driven estimation of its parameters result in relative WER reduction of about 20% compared to phase-insensitive VTS version. The proposed method is also compared to the VTS with constant phase vector and this approximation is shown to be very efficient.