Robust automatic speech recognition with missing and unreliable acoustic data
Speech Communication
On noise masking for automatic missing data speech recognition: A survey and discussion
Computer Speech and Language
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse imputation for noise robust speech recognition using soft masks
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Compressed sensing and source separation
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
IEEE Transactions on Information Theory
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An effective way to increase the noise robustness of automatic speech recognition is to label noisy speech features as either reliable or unreliable (missing) prior to decoding, and to replace the missing ones by clean speech estimates. We present a novel method based on techniques from the field of Compressive Sensing to obtain these clean speech estimates. Unlike previous imputation frameworks which work on a frame-by-frame basis, our method focuses on exploiting information from a large time-context. Using a sliding window approach. denoised speech representations are constructed using a sparse representation of the reliable features in an overcomplete dictionary of clean. fixed-length speech exemplars. We demonstrate the potential of our approach with experiments on the AURORA-2 connected digit database.