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 [BibTeX] [Marc21]
Sparse Component Analysis for Speech Recognition in Multi-Speaker Environment
Type of publication: Conference paper
Citation: Asaei_INTERSPEECH_2010
Booktitle: Proceedings of Interspeech
Year: 2010
Month: 9
Location: Makuhari, Japan
Abstract: Sparse Component Analysis is a relatively young technique that relies upon a representation of signal occupying only a small part of a larger space. Mixtures of sparse components are disjoint in that space. As a particular application of sparsity of speech signals, we investigate the DUET blind source separation algorithm in the context of speech recognition for multi-party recordings. We show how DUET can be tuned to the particular case of speech recognition with interfering sources, and evaluate the limits of performance as the number of sources increases. We show that the separated speech fits a common metric for sparsity, and conclude that sparsity assumptions lead to good performance in speech separation and hence ought to benefit other aspects of the speech recognition chain.
Keywords: Automatic Speech Recognition, Overlapping Speech, Sparse Component Analysis
Projects Idiap
Authors Asaei, Afsaneh
Bourlard, Hervé
Garner, Philip N.
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  • Asaei_INTERSPEECH_2010.pdf