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Traditional noise reduction methods usually are based on the assumption that the short-term statistical distributions of speech and noise are different. Differently from that assumption, we have proposed a noise reduction method based on the assumption that the temporal modulations of noise and speech are different. Two steps are used in the proposed algorithm: one is the temporal modulation contrast...
In this paper, we present the derivation of the backfitting training algorithms for generic p-layer additive F0 models for arbitrary positive integer p. We have presented the special cases of the algorithms with p = 2 and p = 3 that have been successfully applied to the modelings of Japanese and English F0 contours, whereas the derivation of the algorithm was presented only for the two-layer case...
In this paper, we propose a two-step processing algorithm which adaptively normalizes the temporal modulation of speech to extract robust speech feature for automatic speech recognition systems. The first step processing is to normalize the temporal modulation contrast (TMC) of the cepstral time series for both clean and noisy speech. The second step processing is to smooth the normalized temporal...
In this paper, we proposed a robust speech feature extraction algorithm for automatic speech recognition which reduced the noise effect in the temporal modulation domain. The proposed algorithm has two steps to deal with the time series of cepstral coefficients. The first step adopted a modulation contrast normalization to normalize the temporal modulation contrast of both clean and noisy speech to...
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