Dual-channel DNN-based speech enhancement for smartphones

Speech communications in real-world scenarios need high performance enhancement algorithms to address the distortions that can degrade the intelligibility and quality of the speech signal. Current portable devices usually integrate multiple microphones that can conveniently be exploited to improve the signal quality. In this paper we present a dual-microphone speech enhancement approach suitable for smartphones with primary (front) and reference (back) microphones. Our proposal is based on the use of deep neural networks which are able to obtain a non-linear mapping function between noisy and clean speech signals. We explore two different architectures: a feedforward deep neural network (DNN) with temporal context and a gated recurrent unit (GRU) recurrent neural network (RNN). The proposed system is evaluated under different acoustic conditions in close- and far-talk device positions. A comparison with other single- and dual-channel approaches shows that our proposal obtains the best performance in terms of perceptual quality.