On using spectral gradient in conditional MAP criterion for robust voice activity detection
Jae-Hun Choi, Joon‐Hyuk Chang · 2012
In this paper, we propose a novel approach to improve a statistical model-based voice activity detection (VAD) method based on a modified conditional maximum a posteriori (MAP) criterion incorporating the spectral gradient scheme. The proposed conditional MAP incorporates not only the voice activity decision in the previous frame as in Ref. [1] but also the spectral gradient of the observed spectra between the current frame and the past frames to efficiently exploit the inter-frame correlation of voice activity. As a result, the proposed VAD leads to six separate thresholds to be adaptively determined in the likelihood ratio test (LRT) depending on both the previous VAD result and the estimated spectral gradient parameter. Experimental results demonstrate that the proposed approach yields better results compared to those of the previous conditional MAP-based method.