Complex IRM-Aware Training for Voice Activity Detection Using Attention Model

Although many state-of-the-art approaches for improving the accuracy of Voice Activity Detection (VAD) have been proposed, their performance under adverse noise conditions with low Signal-to-Noise Ratio (SNR) remains limited. In this paper, we introduce a novel attention model-based deep neural network (DNN) architecture for VAD which takes advantage of complex Ideal Ratio Mask (cIRM). The proposed model, named AM-cIRM, consists of three sequential modules: extraction of cIRM features from the noisy speech using a DNN-based architecture; combination of cIRM with log-Mel spectrogram features along with temporal contextual extension; and VAD using an attention model that exploits the spectro-temporal information in the transformed features. Experimental results show that the proposed AM-cIRM achieves improved VAD performance when compared to state-of-the-art methods under different noise conditions.