A CMLLR supervector kernel for SVM language recognition

This paper explores the use of constrained maximum likelihood linear regression (CMLLR) transforms as features for language recognition. Modeling is carried out through support vector machine (SVM). This work proposes a novel CMLLR supervector kernel. Results on the NIST LRE09 task show that feature-domain CMLLR transforms contain more language dependent information than model-domain MLLRs, and the proposed CMLLR supervector kernel outperforms some other ones. We also compare our CMLLR-SVM system with some state-of-the-art systems, and combine them for a further improvement.