Phase aware deep neural network for noise robust voice activity detection
Longbiao Wang, Khomdet Phapatanaburi, Zeyan Go, Seiichi Nakagawa, Masahiro Iwahashi, Jianwu Dang · 2017
Phase information is ignored for almost all voice activity detection (VAD). To exploit full information in the original signal, this paper proposes a deep neural network (DNN) using magnitude and phase information (that is, phase aware DNN) to achieve better VAD performance. Mel-frequency cepstral coefficient (MFCC), power-normalized cepstral coefficients (PNCC), instantaneous frequency derivative (IF), baseband phase difference (BPD) and modified group delay cepstral coefficient (MGDCC) are used as magnitude and phase information. The proposed methods were evaluated using CENSREC-1-C database under noise condition. The results show that the phase aware DNN significantly outperforms the DNN using only magnitude information. For DNN-based classifier, the equal error rate (EER) was reduced from 23.70% of MFCC, to 20.43% of joint dual magnitude and single phase features (augmenting PNCC, MGDCC and IF), to 19.92% of joint dual phase and single magnitude feature features (augmenting PNCC, MGDCC and BPD). By combining joint dual magnitude and single phase features with joint dual phase and single magnitude features, the EER was reduced to 19.44%.