Open AccessCross‐domain speaker recognition using domain adversarial siamese network with a domain discriminator
Author(s) -
Chen Zhigao,
Miao Xiaoxiao,
Xiao Runqiu,
Wang Wenchao
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2020.0673
Subject(s) - discriminator , computer science , discriminative model , speech recognition , domain (mathematical analysis) , artificial intelligence , test data , nist , adversarial system , set (abstract data type) , test set , data set , pattern recognition (psychology) , machine learning , mathematics , telecommunications , detector , programming language , mathematical analysis
With the widespread use of automatic speaker recognition in realistic world, it suffers a lot when there is a domain mismatch, including channel, language, distance etc. Recent research studies have introduced the adversarial‐learning mechanism into deep neural networks to reduce the distribution mismatch between different domains. However, they only aligned the domain distributions between the background training and evaluation data. Few focused on the diverse distribution underlying the enrol and test data. In this Letter, the authors propose a domain adversarial siamese (DAS) network trying to eliminate the domain influence on speech representation. Specifically, they feed a network with speech pairs from the same speaker. Then a domain discriminator is introduced to capture the domain consistence or difference between pairs. Final embeddings become domain‐invariant and more speaker‐discriminative. A cross‐channel data set is sort out from NIST speaker recognition evaluation and more experiments are conducted on AISHELL‐Wake‐Up‐1 data set. Results show that DAS performs equally effective with typical domain adversarial methods, improving results at least 10 % on energy efficiency rating. Furthermore, it is proved to be more valid for scenarios such as unbalanced data amount and unknown domain, achieving relatively 11 % improvements.