A sparse representation approach for perceptual quality improvement of separated speech

Speech separation based on time-frequency masking has been shown to improve intelligibility of speech signals corrupted by noise. A perceived weakness of binary masking is the quality of separated speech. In this paper, an approach for improving the perceptual quality of separated speech from binary masking is proposed. Our approach consists of two stages, where a binary mask is generated in the first stage that effectively performs speech separation. In the second stage, a sparse-representation approach is used to represent the separated signal by a linear combination of Short-time Fourier Transform (STFT) magnitudes that are generated from a clean speech dictionary. Overlap-and-add synthesis is then used to generate an estimate of the speech signal. The performance of the proposed approach is evaluated with the Perceptual Evaluation of Speech Quality (PESQ), which is a standard objective speech quality measure. The proposed algorithm offers considerable improvements in speech quality over binary-masked noisy speech and other reconstruction approaches.