Technical Program

Paper Detail

Session:Voice Activity Detection and Speech Segmentation
Time:Wednesday, May 19, 13:00 - 15:00
Presentation: Poster
Topic: Speech Processing: Speech Analysis
Authors: Philip Garner; Canon, Inc. 
 Toshiaki Fukada; Canon, Inc. 
 Yasuhiro Komori; Canon, Inc. 
Abstract: The Voice Activity Detection (VAD) problem is placed into a decision theoretic framework, and the Gaussian VAD model of Sohn et al. is then shown to fit well with the framework. It is argued that the Gaussian model can be made more robust to correlation and expected spectral shapes of speech and noise by using a differential spectral representation. Such a model is formulated theoretically. The differential spectral VAD is then shown by experiment to be consistently superior to the basic Gaussian VAD in a speech recognition setting, especially for noisy environments.

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