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Language identification using Gaussian mixture model tokenization
January 1, 2002
Conference Paper
Published in:
Proc. IEEE Int. Conf., on Acoustics, Speech and Signal Processing, ICASSP, Vol. I, 13-17 May 2002, pp. I-757 - I-760.
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Summary
Phone tokenization followed by n-gram language modeling has consistently provided good results for the task of language identification. In this paper, this technique is generalized by using Gaussian mixture models as the basis for tokenizing. Performance results are presented for a system employing a GMM tokenizer in conjunction with multiple language processing and score combination techniques. On the 1996 CallFriend LID evaluation set, a 12-way closed set error rate of 17% was obtained.
Summary
Phone tokenization followed by n-gram language modeling has consistently provided good results for the task of language identification. In this paper, this technique is generalized by using Gaussian mixture models as the basis for tokenizing. Performance results are presented for a system employing a GMM tokenizer in conjunction with multiple...
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