Speech recognitionwith speech density estimation by the Dirichlet Process Mixture

This paper shows a method for the modeling of speech signal distributions based on Dirichlet process mixtures (DPM) and the estimation of noise sequences based on particle filtering. In real situations, the speech recognition rate degrades miser ably because of the effect of environmental noises, reflected waves and so on. To improve the speech recognition rate, a technique for the estimation of noise sequences is necessary. In this paper, the distribution of the clean speech is modeled using the DPM instead of the traditional model, which is a Gaussian mixture model (GMM). Speech signal sequences are generated according to the mean and covariance generated from the DPM. Then, noise signal sequences are estimated with a particle filter. The proposed method using extended Kalman filter (EKF) can improve the speech recognition rate significantly in the low SNR region. Applying unscented Kalman filter (UKF), better results can be obtained in also the high SNR.