Abstract
This paper addresses the main 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 through a sequential importance sampling step, then a residual resampling step, and finally a Markov chain Monte Carlo step with Metropolis-Hastings sampling. The estimated noise sequence is applied to MMSE-based clean speech estimation method. The evaluations were conducted on speech recognition in highly nonstationary noise environments. In the evaluation results, we observed that the proposed method improves speech recognition accuracy in non-stationary noise environments over noise compensation with stationary noise assumptions.
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