Noise robust model-based voice activity detection
Ángel de la Torre, Javier Ramı́rez, Carmen Benı́tez, Jose Carlos Segura, Luz García, Antonio J. Rubio · 2006
We propose a model-based VAD derived from the Vector Taylor Series (VTS) approach. A Gaussian mixture (trained with clean speech) is used in order to provide an appropriate decision rule for speech/non-speech detection. Additionally, VTS approach adapts the Gaussian mixture to noise conditions, yielding a stable perfor- mance for a wide range of SNRs. We have evaluated its ability for speech/non-speech detection and also its application for robust speech recognition. When compared to other VAD methods, the proposed VAD shows the best trade-off in speech/non-speech de- tection. When applied for Wiener Filtering and for frame drop- ping, the proposed VAD also provides the best recognition results. Index Terms: voice activity detection (VAD), vector Taylor series approach (VTS), Gaussian mixture, Wiener filtering.