Low SNR speech enhancement with DNN based phase estimation

In low Signal-to-Noise Ratio environment phase information is one of the important factor and therefore this article consider the importance of clean phase in single channel speech enhancement technique. The proposed method uses Deep Neural Network based regression model to estimate clean phase and clean amplitude for speech reconstruction. Experiments are conducted over five different noises such as factory, restaurant, car, airport and babble at different levels and result are evaluated using objective quality measures like Perceptual Evaluation of Speech Quality, Weighted Spectral Slope, Cepstrum Distance, frequency weighted segmented Signal-to-Noise Ratio and Log Likelihood Ratio. The overall quality of speech improved for factory noise by \(12\%\), restaurant noise by \(8\%\), car noise by \(13\%\), airport noise by \(10\%\) and babble noise by \(14\%\) respectively.