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 miserably 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 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 can improve the speech recognition rate significantly in the low SNR region.
speech recognition, Kalman filter, Dirichlet process mixture, density estimation, particle filter.
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