N-Best Tokenization in a GMM-SVM Language Identification System

N-best or lattice-based tokenization has been widely used in speech-related classification tasks. In this paper, we extended the n-best tokenization approach to GMM-based language identification systems with either maximum likelihood (ML) trained or SVM-based language models. We explored the effect of n-best tokenization in training or testing, and its interaction with n-gram order and system fusion. We showed that for both systems, the n-best tokenization gives good performance improvement. However, the SVM-based system benefited from both n-best training and test while the ML-trained system can only benefit from n-best training. Results show n-best tokenization can reduce the relative EER of our best GMM-SVM system by about 5% for 30s and 10s tests.