LEARNING SPECTRAL MAPPING FOR SPEECH DEREVERBERATION

Reverberation distorts human speech and usually has negative effects on speech intelligibility, especially for hearing-impaired listeners. It also causes performance degradation in automatic speech recognition and speaker identification systems. Therefore, the dereverberation problem must be dealt with in daily listening environments. We propose to use deep neural networks (DNNs) to learn a spectral mapping from the reverberant speech to the anechoic speech. The trained DNN produces the estimated spectral representation of the corresponding anechoic speech. We demonstrate that distortion caused by reverberation is substantially attenuated by the DNN whose outputs can be resynthesized to the dereverebrated speech signal. The proposed approach is simple, and our systematic evaluation shows promising dereverberation results, which are significantly better than those of related systems.