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Paper data
Multichannel Voice Detection in Adverse Environments

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

Segmentation and Voice Detection

Paper abstract
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.

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