Multi-Task Learning U-Net for Single-Channel Speech Enhancement and Mask-Based Voice Activity Detection

In this paper, a multi-task learning U-shaped neural network (MTU-Net) is proposed and applied to single-channel speech enhancement (SE). The proposed MTU-based SE method estimates an ideal binary mask (IBM) or an ideal ratio mask (IRM) by extending the decoding network of a conventional U-Net to simultaneously model the speech and noise spectra as the target. The effectiveness of the proposed SE method was evaluated under both matched and mismatched noise conditions between training and testing by measuring the perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI). Consequently, the proposed SE method with IRM achieved a substantial improvement with higher average PESQ scores by 0.17, 0.52, and 0.40 than other state-of-the-art deep-learning-based methods, such as the deep recurrent neural network (DRNN), SE generative adversarial network (SEGAN), and conventional U-Net, respectively. In addition, the STOI scores of the proposed SE method are 0.07, 0.05, and 0.05 higher than those of the DRNN, SEGAN, and U-Net, respectively. Next, voice activity detection (VAD) is also proposed by using the IRM estimated by the proposed MTU-Net-based SE method, which is fundamentally an unsupervised method without any model training. Then, the performance of the proposed VAD method was compared with the performance of supervised learning-based methods using a deep neural network (DNN), a boosted DNN, and a long short-term memory (LSTM) network. Consequently, the proposed VAD methods show a slightly better performance than the three neural network-based methods under mismatched noise conditions.