Research on Joint Adaptation for Phonotactic Language Recognition
基于音素解码的语种识别系统联合自适应算法研究

For language recognition in real application,a variety of non-language sources(i.e.,channel,content,etc.) will induce mismatch between training and test utterances,which affects the recognition accuracy.This paper introduces joint adaptation to deal with the mismatch problem for the phone recognition followed by vector space model(PRVSM) system.We investigate three adaptation methods in different stage of the system:1) acoustic model adaptation using constrained maximum likelihood linear regression(CMLLR);2) phonotactic feature adaptation using the universal N-grams;3) adapt-SVM for the vector space model(VSM).The joint adaptation is carried out by combining these methods and significant improvements can be obtained.Experiments on the NIST LRE 2009 evaluation corpus show that there are relative decreases of 18% ～23%,12%～20% and 5%～9% in EER for the 30s,10s and 3s test conditions,respectively.