Robust speech recognition under noisy environments based on selection of multiple noise suppression methods

Summary form only given. To achieve high recognition performance for a wide variety of noise and for a wide range of signal-to-noise ratio, this paper presents the integration of four noise reduction algorithms: spectral subtraction with smoothing of time direction; temporal domain SVD-based speech enhancement; GMM-based speech estimation; and KLT-based comb-filtering. In this paper, we investigated the optimal suppression method for each noise condition, and then also developed the method of choosing the optimal method automatically for unknown noise. Recognition results on the AURORA-2J task show the effectiveness of our proposed method.