A Non-stationary Noise Suppression Method Based on Particle Filtering and Polyak Averaging(Speech Recognition, <Special Section> Statistical Modeling for Speech Processing)

概要

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This paper addresses a speech recognition problem in nonstationary noise environments: the estimation of noise sequences. To solve this problem, we present a particle filter-based sequential noise estimation method for front-end processing of speech recognition in noise. In the proposed method, a noise sequence is estimated in three stages: a sequential importance sampling step, a residual resampling step, and finally a Markov chain Monte Carlo step with Metropolis-Hastings sampling. The estimated noise sequence is used in the MMSE-based clean speech estimation. We also introduce Polyak averaging and feedback into a state transition process for particle filtering. In the evaluation results, we observed that the proposed method improves speech recognition accuracy in the results of non-stationary noise environments a noise compensation method with stationary noise assumptions.
社団法人電子情報通信学会の論文
2006-03-01