Speech recognition and enhancement by a nonstationary AR HMM with gain adaptation under unknown noise

A gain-adapted speech recognition in unknown noise is developed in time domain. The noise is assumed to be the colored noise. The nonstationary autoregressive (NAR) hidden Markov model (HMM) used to model clean speeches. The nonstationary AR is modeled by polynomial functions with a linear combination of M known basis functions. Enhancement using multiple Kalman filters is performed for the gain contour of speech and estimation of noise model when only the noisy signal is available.