Phase-sensitive and recognition-boosted speech separation using deep recurrent neural networks

Separation of speech embedded in non-stationary interference is a challenging problem that has recently seen dramatic improvements using deep network-based methods. Previous work has shown that estimating a masking function to be applied to the noisy spectrum is a viable approach that can be improved by using a signal-approximation based objective function. Better modeling of dynamics through deep recurrent networks has also been shown to improve performance. Here we pursue both of these directions. We develop a phase-sensitive objective function based on the signal-to-noise ratio (SNR) of the reconstructed signal, and show that in experiments it yields uniformly better results in terms of signal-to-distortion…

• TB
  Türkiye Bilimsel ve Teknolojik Araştırma Kurumu
• FP
  Fondo para la Investigación Científica y Tecnológica