Voice Activity Detection Based on GM(1,1) Model
Cheng‐Hsiung Hsieh, Ting-Yu Feng, Ren-Hsien Huang · 2007
In this paper, a novel approach to apply GM(1,1) model in voice activity detection (VAD) is presented. The approach is termed as grey VAD (GVAD). In GVAD, GM(1,1) model is used to estimate noise in noisy speech and therefore signal where the additive signal model is assumed. By estimated noise and signal, the signal-to-noise ratio (SNR) is calculated. Based on an adaptive threshold, speech and non-speech segments are determined. The proposed GVAD is performed in the time-domain and thus has low computational complexity. In the simulation, GVAD is verified by cases with non-stationary additive white Gaussian noise and is compared with VAD in G 729 and GSM AMR. The results indicate that the proposed GVAD is able to detect voice activity appropriately. In the given examples, the performance of GVAD is better than VAD in G 729 and GSM AMR.