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In this paper, we propose an acoustic model combination (AMC) technique for reducing a mismatch between training and testing conditions of an automatic speech recognition (ASR) system in a multi-channel noisy environment. In our previous work, we proposed a hidden Markov model (HMM)-based mask estimation method for multi-channel source separation using two microphones, where HMMs were adopted for...
In this paper, we propose a new mask estimation method for the computational auditory scene analysis (CASA) of speech using two microphones. The proposed method is based on a hidden Markov model (HMM) in order to incorporate an observation that the mask information should be correlated over contiguous analysis frames. In other words, HMM is used to estimate the mask information represented as the...
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