Learning to maximize signal-to-noise ratio for reverberant speech segregation

Monaural speech segregation in reverberant environments is a very difficult problem. We develop a supervised learning approach by proposing an objective function that directly relates to the computational goal of maximizing signal-to-noise ratio. The model trained using this new objective function yields significantly better results for time-frequency unit labeling. In our segregation system, a segmentation and grouping framework is utilized to form reliable segments under reverberant conditions and organize them into streams. Systematic evaluations show very promising results.