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

Outline

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.

Robust feature extraction Feature Transformation Feature Compensation Feature Reconstruction

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

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

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

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.

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

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.

feature enhencement Solve the mismatch problem