An Adaptation Method in Noise Mismatch Conditions for DNN-Based Speech Enhancement

DOI

10.2991/ncce-18.2018.118How to use a DOI?

Keywords

Enhancement; adaptation method; DNN; PESQ; segSNR; STOI.

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 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 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 29% benefits of STOI at most and provided consistent improvement in PESQ and segSNR against the baseline systems.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Siying Xu
AU  - Dan Qu
AU  - Xingyan Long
PY  - 2018/05
DA  - 2018/05
TI  - An Adaptation Method in Noise Mismatch Conditions for DNN-Based Speech Enhancement
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 719
EP  - 726
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.118
DO  - 10.2991/ncce-18.2018.118
ID  - Xu2018/05
ER  -