4th International Conference on Spoken Language Processing

Philadelphia, PA, USA
October 3-6, 1996

Robust Automatic Speech Recognition Using a Multi-channel Signal Separation Front-End

Kuan-Chieh Yen, Yunxin Zhao

Beckman Institute and Department of Electrical and Computer Engineering, University of Illinois at Urbara-Champaign, IL, USA

A multi-channel signal separation front-end for robust automatic speech recognition under time-varying interference conditions is developed. The speech signals acquired by a dual-channel system are restored by adaptive decorrelation filtering, and then examined by a time-domain or frequency-domain source signal detection technique to determine the active regions of each source signal. The front-end is integrated with an HMM-based speaker-independent continuous speech recognition system by providing the restored signals within the active regions for recognition. Under a simulated room acoustic condition, the overall system shows very promising performance. For the conditions with SNR above -10 dB, the achieved word recognition accuracies are very close to that of the interference-free condition.

Full Paper
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Bibliographic reference.  Yen, Kuan-Chieh / Zhao, Yunxin (1996): "Robust automatic speech recognition using a multi-channel signal separation front-end", In ICSLP-1996, 1337-1340.