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In this work, we present a new mask estimation technique that uses a neural network classifier to determine the reliability of spectrographic elements. In addition some different kinds of features used for classification were compared that make no assumptions about the corrupting noise signal, but rather exploit spectrographic characteristics of the speech signal. The performance of the proposed method...
This paper presents a new approach to consider the correlation of noise and clean speech signals, when an autocorrelation-based set of features are used. Autocorrelation-based features have recently been used in several cases for achieving robustness in automatic speech recognition (ASR) systems. Such methods usually consider the clean speech and noise to be uncorrelated. However, when some correlation...
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