The Design of Backend Classifiers in PPRLM System for Language Identification

Abstract

The design approach for classifying the backend features of the PPRLM (Parallel Phone Recognition and Language Modeling) system is demonstrated in this paper. A variety of features and their combinations extracted by language dependent recognizers were evaluated based on the National Institute of Standards and Technology (NIST) Language Recognition Evaluation (LRE) 2003 corpus. Three well-known classifiers: Gaussian Mixture Model (GMM), Support Vector Machine (SVM), and feed forward neural network (NN) are proposed to compartmentalize these high level features which are generated by n-gram language model scoring and one pass decoding based on acoustic model in PPRLM system. Finally, the log-likelihood radio (LLR) normalization is applied to backend processing to the target language scores and the performance of language recognition is enhanced.