Deep neural network‐based linear predictive parameter estimations for speech enhancement
Yaxing Li, Sangwon Kang · IET Signal Processing · 2016
This study presents a speech enhancement technique to improve noise corrupted speech via deep neural network (DNN)‐based linear predictive (LP) parameter estimations of speech and noise. With regard to the LP coefficient estimation, an enhanced estimation method using a DNN with multiple layers was proposed. Excitation variances were then estimated via a maximum‐likelihood scheme using observed noisy speech and estimated LP coefficients. A time‐smoothed Wiener filter was further introduced to improve the enhanced speech quality. Performance was evaluated via log spectral distance, a composite multivariate adaptive regression splines modelling‐based measure, and a segmental signal‐to‐noise ratio. The experimental results revealed that the proposed scheme outperformed competing methods.