Model-based Compressive Sensing for Multi-party Distant Speech Recognition
| Type of publication: | Conference paper |
| Citation: | Asaei_ICASSP_2011 |
| Publication status: | Published |
| Booktitle: | 2011 IEEE International Conference on Acoustics, Speech and Signal Processing |
| Year: | 2011 |
| Crossref: | Asaei_Idiap-RR-04-2011: |
| Abstract: | We leverage the recent algorithmic advances in compressive sensing, and propose a novel source separation algorithm for efficient recovery of convolutive speech mixtures in spectro-temporal domain. Compared to the common sparse component analysis techniques, our approach fully exploits structured sparsity models to obtain substantial improvement over the existing state-of-the-art. We evaluate our method for separation and recognition of a target speaker in a multi-party scenario. Our results provide compelling evidence of the effectiveness of sparse recovery formulations in speech recognition. |
| Keywords: | Model-Based Compressive Sensing, Overlapping Speech, Sparse Component Analysis, Sparse Recovery, speech recognition |
| Projects |
Idiap FP 7 |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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