Year: 2018
Type: article
Abstract: The deep learning based speech enhancement has shown considerable success.However, it still suffers performance degradation under mismatch conditions.In this paper, an adaptation method is proposed to improve the performance under noise mismatch conditions.Firstly, we advise a noise aware training by supplying identity vectors (i-vectors) as parallel input features to adapt deep neural network (DNN) acoustic models with the target noise.Secondly, given a small amount of adaptation data, the noise-dependent DNN is obtained by using L 2 regularization from a noise-independent DNN, and forcing the estimated masks to be close to the unadapted condition.Finally, experiments were carried out on different noise and SNR conditions, and the proposed method has achieved significantly 0.1%-9.6%benefits of STOI, and provided consistent improvement in PESQ and segSNR against the baseline systems.
Authors: Xu Si-Ying, Niu Tong, Qu Dan, Long Xing-Yan
Language: English
Topic: Speech and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2809520515