Language-aware PLDA for multilingual speaker recognition
Askar Rozi, Dong Wang, Lantian Li, Thomas Fang Zheng · 2016
Multilingual speaker recognition involves multilingual speech data in model training, which empowers the system to handle recognition requests in multiple languages. The multilingual training approach augments data from multiple languages, but inevitably introduces probability dispersion, due to the more complex language conditions. This paper proposes a language-aware training approach for PLDA which involves language information when training the PLDA model. The proposed approach has been evaluated with the i-vector/PLDA framework using the CSLT-CUDGT2014 Chinese-Uyghur bilingual speech database. The experimental results show that the language PLDA training resulted in a relative EER reduction of 15.38% in the Chinese test and 20.07% in the Uyghur test.