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In this paper, we address the problem of speech recognition in
the presence of additive noise. We investigate the applicability
and efficacy of auditory masking in devising a robust front end
for noisy features. This is achieved by introducing a masking
factor into the Vector Taylor Series (VTS) equations. The resultant
first order VTS approximation is used to compensate the parameters
of a clean speech model and a Minimum Mean Square
Error (MMSE) estimate is used to estimate the clean speech

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Ever since the deep neural network (DNN)-based acoustic model appeared, the recognition performance of automatic peech recognition has been greatly improved. Due to this achievement, various researches on DNN-based technique for noise robustness are also in progress. Among these approaches, the noise-aware training (NAT) technique which aims to improve the inherent robustness of DNN using noise estimates has shown remarkable performance. However, despite the great performance, we cannot be certain whether NAT is an optimal method for sufficiently utilizing the inherent robustness of DNN.

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In practical situations, the emotional speech utterances are often collected from different devices and conditions, which will obviously affect the recognition performance. To address this issue, in this paper, a novel transfer non-negative matrix factorization (TNMF) method is presented for cross-corpus speech emotion recognition. First, the NMF algorithm is adopted to learn a latent common feature space for the source and target datasets.

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