Open AccessChannel Mismatch Speaker Verification Based on Deep Learning and PLDA
Author(s) -
Pengqi Li,
Guanyu Li,
Jiao Han,
Zhi Tiankai,
Di Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1682/1/012056
Subject(s) - computer science , robustness (evolution) , speaker recognition , speech recognition , speaker verification , channel (broadcasting) , compensation (psychology) , artificial intelligence , artificial neural network , pattern recognition (psychology) , telecommunications , psychology , biochemistry , chemistry , psychoanalysis , gene
At present, speaker recognition technology is developing continuously, but in real world, existing speaker recognition algorithms are generally disturbed by channel factors. With the increase of current data volume, neural network has also become a key technology for speaker recognition. This paper introduction a speaker recognition method based on supervised training and a channel compensation algorithm. The experimental results show that this method combined with channel compensation algorithm can remove the influence of channel noise and has a good speaker recognition effect. The experimental results show that the combination of x-vector and PLDA channel compensation algorithm can obtain better recognition rate and ensure the robustness of the algorithm.