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Discriminative training of GMM based on Maximum Mutual Information for language identification

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4 Author(s)
Qu Dan ; Dept. of Signal Analyzing Eng., Inf. Eng. Univ., Zhengzhou ; Wang Bingxi ; Yan Honggang ; Dai Guannan

In this paper, a discriminative training procedure based on maximum mutual information (MMI) for a Gaussian mixture model (GMM) language identification system is described. The idea is to find the model parameters lambda that minimize the conditional entropy Hlambda (C | X) of the random variable C given the random variable X , which means minimize the uncertainty in knowing what language was spoken given access to the utterance in X . The implementation of the proposal is based on the generalized probabilistic descent (GPD) algorithm formulated to estimate the GMM parameters. The evaluation is conducted using the OGI multi-language telephone speech corpus. The experimental results show such system is very effective in language identification tasks

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Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on  (Volume:1 )

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