An Adaptation Method in Noise Mismatch Conditions for DNN-Based Speech Enhancement
Siying Xu, Dan Qu, Xingyan Long · Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018) · 2018
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 (ivectors) as parallel input features to adapt DNN acoustic models with the target noise.Secondly, given a small amount adaptation data, the noise-dependent DNN is obtained by using Euclidean distance regularization from a noiseindependent 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 29% benefits of STOI at most and provided consistent improvement in PESQ and segSNR against the baseline systems.