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Survey of ICASSP 2013section: feature for robust automatic speech
recognition
Repoter: Yi-Ting Wang2013/06/19
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Robust automatic speech recognition
• Over the years, much effort has been devoted on developing techniques for noise robust Automatic Speech Recognition(ASR).
• The goal is to make the ASR system more resistant to the mismatch between training and testing condition.
• Noise reduction techniques:– Speech enhancement at the signal level– Robust feature extraction– Adapting the back-end models.
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A robust frontend for ASR : combining denoising, noise masking and feature normalization
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Filtering on the temporal probability sequence in histogram equalization for robust speech recognition
HEQ FHEQ iXrefi xFFy 1
kiXk
krefi xFhFy 1
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Ideal ratio mask estimation using deep neural networks for robust speeh recognition
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Solve the problem in reverberant environments
• It is well known that the distortions caused by reverberation, background noise, etc., are highly nonlinear in the cepstral domain.
• Dereverberation via suppression and enhancement can be applied for reverberation compensation.
• The drawback is undesirable if the late reverberation is not estimated precisely.
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Noise model transfer using affine transformation with application to large vocabulary reverberant speech recognition
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Joint sparse representation based cepstral-domain dereverberation for distant-talking speech recognition
• Most existing sparse representation methods only consider sparse modeling in a single signal space, and few considers dictionary learning across different signal spaces.