survey of icassp 2013 section: feature for robust automatic speech recognition repoter: yi-ting wang...

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Survey of ICASSP 2013 section: feature for robust automatic speech recognition Repoter: Yi-Ting Wang 2013/06/19

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Page 1: Survey of ICASSP 2013 section: feature for robust automatic speech recognition Repoter: Yi-Ting Wang 2013/06/19

Survey of ICASSP 2013section: feature for robust automatic speech

recognition

Repoter: Yi-Ting Wang2013/06/19

Page 2: Survey of ICASSP 2013 section: feature for robust automatic speech recognition Repoter: Yi-Ting Wang 2013/06/19

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.

Page 3: Survey of ICASSP 2013 section: feature for robust automatic speech recognition Repoter: Yi-Ting Wang 2013/06/19

A robust frontend for ASR : combining denoising, noise masking and feature normalization

Page 4: Survey of ICASSP 2013 section: feature for robust automatic speech recognition Repoter: Yi-Ting Wang 2013/06/19

Filtering on the temporal probability sequence in histogram equalization for robust speech recognition

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krefi xFhFy 1

Page 5: Survey of ICASSP 2013 section: feature for robust automatic speech recognition Repoter: Yi-Ting Wang 2013/06/19

Ideal ratio mask estimation using deep neural networks for robust speeh recognition

Page 6: Survey of ICASSP 2013 section: feature for robust automatic speech recognition Repoter: Yi-Ting Wang 2013/06/19

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.

Page 7: Survey of ICASSP 2013 section: feature for robust automatic speech recognition Repoter: Yi-Ting Wang 2013/06/19

Noise model transfer using affine transformation with application to large vocabulary reverberant speech recognition

Page 8: Survey of ICASSP 2013 section: feature for robust automatic speech recognition Repoter: Yi-Ting Wang 2013/06/19

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.