Kohei Machida

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Non-negative Matrix Factorization (NMF) is a method of multivariate analysis which factorizes a non-negative matrix into two non-negative matrices. While conventional NMF algorithms use the Euclidian distance or the Kullback-Leibler divergence as cost functions, those methods fail to extract latent structure or interpretable information from the matrix when(More)
In this paper, we propose a method for noise-robust speech recognition in a home environment based on noise modeling and parallel decoding. There are three basic ideas of the proposed method. First, we model the noise signals observed in the environment using a GMM. Second, we generate multiple noise-reduced signals using the mean vectors of the GMM and(More)
We propose a robust speech recognition method under noisy environments using multiple microphones based on asynchronous and intermittent observation. In asynchronous and intermittent observation, the noise spectrum is estimated by the environmental noise observed in fragments from multiple microphones, and spectral subtraction is performed by this estimated(More)
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