Multiple-target deep learning for LSTM-RNN based speech enhancement
Lei Sun, Jun Juan Du, Li-Rong Dai, Chin‐Hui Lee · 2017
In this study, we explore long short-term memory recurrent neural networks (LSTM-RNNs) for speech enhancement. First, a regression LSTM-RNN approach for a direct mapping from the noisy to clean speech features is presented and verified to be more effective than deep neural network (DNN) based regression techniques in modeling long-term acoustic context. Then, a comprehensive comparison between the proposed direct mapping based LSTM-RNN and ideal ratio mask (IRM) based LSTM-RNNs is conducted. We observe that the direct mapping framework achieves better speech intelligibility at low signal-to-noise ratios (SNRs) while the IRM approach shows its superiority at high SNRs. Accordingly, to fully utilize this complementarity, a novel multiple-target joint learning approach is designed. The experiments under unseen noises show that the proposed framework can consistently and significantly improve the objective measures for both speech quality and intelligibility.