Open AccessDeep neural network‐based linear predictive parameter estimations for speech enhancement
Author(s) -
Li Yaxing,
Kang Sangwon
Publication year - 2017
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2016.0477
Subject(s) - speech enhancement , computer science , linear prediction , wiener filter , linear predictive coding , speech recognition , artificial neural network , noise (video) , estimation theory , speech processing , pattern recognition (psychology) , artificial intelligence , algorithm , noise reduction , image (mathematics)
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.