The ICSI 2007 Language Recognition System.
Christian Muller and Joan-Isaac Biel
Abstract
In this paper, we describe the ICSI 2007 language recognition system. The system constitutes a variant of the classic PPRLM (parallel phone recognizer followed by language modeling) approach. We used a combination of frame-by-frame multilayer perceptron (MLP) phone classifiers for English, Arabic, and Mandarin and one open loop hidden Markov Model (HMM) phone recognizer (trained on English data). The maximum likelihood language modeling is substituted by support-vector-machines (SVMs) as a more powerful, discriminative classification method. Rank normalization is used as a normalization method superior to mean-variance normalization. Results are presented on the NIST 2005 language recognition evaluation (LRE05) set and a test set taken from the LRE07 training corpus. The average NIST cost of the system on the LRE05 set is 0.0886
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