Performance of distant-talking speech recognizers in real noisy environments can be increased using a microphone array. In this work we propose an N-best extension of the Limabeam algorithm, which is a likelihood-based adaptive filter-and-sum beamformer. We show that this algorithm can be used to optimize the noisy acoustic features using in parallel the N-best hypothesized transcriptions generated at a first recognition step. The parallel and independent optimizations increase the likelihood of minimal word error rate hypotheses and the resulting N-best hypotheses list is automatically re-ranked. Results show improvements over delay-and-sum beamforming and Unsuper- vised Limabeam on a real database with considerable amount of noise and limited reverberation.

Improving robustness of a likelihood-based beamformer in a real environment for automatic speech recognition

Omologo, Maurizio
2006

Abstract

Performance of distant-talking speech recognizers in real noisy environments can be increased using a microphone array. In this work we propose an N-best extension of the Limabeam algorithm, which is a likelihood-based adaptive filter-and-sum beamformer. We show that this algorithm can be used to optimize the noisy acoustic features using in parallel the N-best hypothesized transcriptions generated at a first recognition step. The parallel and independent optimizations increase the likelihood of minimal word error rate hypotheses and the resulting N-best hypotheses list is automatically re-ranked. Results show improvements over delay-and-sum beamforming and Unsuper- vised Limabeam on a real database with considerable amount of noise and limited reverberation.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/29689
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