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Proceedings Paper

Speaker/speech recognition using microphone arrays and neural networks
Author(s): Qiguang Lin; ChiWei Che; Ea-Ee Jan; James L. Flanagan
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Paper Abstract

Hands-free operation of speech processing equipment is sometimes desired so that the user is unencumbered by hand-held or body-worn microphones. This paper explores the use of array microphones and neural networks (MANN) for robust speech/speaker recognition in a reverberant and noisy environment. Microphone arrays provide high-quality, hands-free sound capture at distances, and neural network processors compensate for environmental interference by transforming speech features of the array input to those of close-talking microphone input. The MANN system is evaluated using both computer-simulated degraded speech and real- room collected speech. It is found that the MANN system is capable of elevating recognition accuracies under adverse conditions, such as room reverberation, noise interference, and mismatch between the training and testing conditions, to levels comparable to those obtained with close-talking microphone input under a matched training and testing condition.

Paper Details

Date Published: 25 October 1994
PDF: 12 pages
Proc. SPIE 2277, Automatic Systems for the Identification and Inspection of Humans, (25 October 1994); doi: 10.1117/12.191874
Show Author Affiliations
Qiguang Lin, Rutgers Univ. (United States)
ChiWei Che, Rutgers Univ. (United States)
Ea-Ee Jan, Rutgers Univ. (United States)
James L. Flanagan, Rutgers Univ. (United States)

Published in SPIE Proceedings Vol. 2277:
Automatic Systems for the Identification and Inspection of Humans
Richard J. Mammone; J. David Murley Jr., Editor(s)

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