Maximum Margin Clustering Based Statistical VAD With Multiple Observation Compound Feature

In this letter, we propose a new robust feature and an unsupervised learning approach for statistical voice activity detection (VAD). Maximum margin clustering (MMC), as an unsupervised classifier, can improve the robustness of support vector machine (SVM) based VAD while requiring no data labeling for model training. In the MMC framework, the multiple observation compound feature (MO-CF) is proposed to improve accuracy. MO-CF is composed of two subfeatures-multiple observation signal-to-noise ratio (MO-SNR) and multiple observation maximum probability (MO-MP). The contributions of the two subfeatures are balanced by a factor which is chosen to yield the largest area under the ROC curve (AUC) of the performance. The proposed approach obtains improved performance over seven commonly used VAD techniques in the experiments covering various noisy scenarios with low SNRs.