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A Non-stationary Noise Suppression Method Based on Particle Filtering and Polyak Averaging Masakiyo FUJIMOTO Satoshi NAKAMURA Publication IEICE TRANSACTIONS on Information and Systems Vol.E89-D No.3 pp.922-930 Publication Date: 2006/03/01 Online ISSN: 1745-1361 DOI: 10.1093/ietisy/e89-d.3.922 Print ISSN: 0916-8532 Type of Manuscript: Special Section PAPER (Special Section on Statistical Modeling for Speech Processing) Category: Speech Recognition Keyword: noisy speech recognition, non-stationary noise, sequential estimation, particle filter, Polyak averaging and feedback, Full Text: PDF(1.8MB)>>
Summary: This paper addresses a speech recognition problem in non-stationary 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. | ||||||||||
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