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Paper data
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Title:
Multichannel Voice Detection in Adverse Environments

Author(s):
Rosca Justinian, Siemens Corporate Research
Balan Radu, Siemens Corporate Research
Fan Ning Ping, Siemens Corporate Research
Beaugeant Christophe, Siemens AG
Gilg Virginie, Siemens AG

Page numbers in the proceedings:
Volume I pp 251-254

Session:
Segmentation and Voice Detection

Paper abstract
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Detecting when voice is or is not present is an outstanding problem for speech transmission, enhancement and recognition. Here we present a novel multichannel source activity detector that exploits the spatial localization of the target audio source. The detector uses an array signal processing technique to maximize the signal-to-interference ratio for the target source thus decreasing the activity detection error rate. We compare our two-channel voice activity detector (VAD) with the AMR voice detection algorithms on real data recorded in a noisy car environment. The new algorithm shows improvements in error rates of 55-70% compared to the state-of-the-art adaptive multi-rate algorithm AMR2 used in present voice transmission technology.

Paper
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A PDF version is available here

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