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 [BibTeX] [Marc21]
Non-linear mapping for multi-channel speech separation and robust overlapping speech recognition
Type of publication: Conference paper
Citation: Li_ICASSP_2009
Booktitle: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Year: 2009
Abstract: This paper investigates a non-linear mapping approach to extract robust features for ASR and separation of overlapping speech. Based on our previous studies, we continue to use two additional sound sources, namely, from the target and interfering speakers. The focues of this work are: 1) We investigate the feature mapping between different domains with the consideration of MMSE criterion and regression optimizations, demonstrating the mapping of log mel-filterbank energies to MFCC can be exploited to improve the effectiveness of the regression; 2) We investigate the data-driven filtering for the speech separation by using the mapping method, which can be viewed as a generalized log spectral subtraction and results in better separation performance. We demonstrate the effectiveness of the proposed approach through extensive evaluations on the MONC corpus, which includes both non-overlapping single speaker and overlapping multi-speaker conditions.
Keywords: binary masking, microphone array, neural network, overlapping speech recognition, speech separation
Projects Idiap
Authors Li, Weifeng
Dines, John
Magimai.-Doss, Mathew
Bourlard, Hervé
Added by: [UNK]
Total mark: 0
  • Li_ICASSP_2009.pdf