Language identification using Gaussian mixture model tokenization

被引：0

作者：

Torres-Carrasquillo, PA ^{[1
]}

Reynolds, DA ^{[1
]}

Deller, JR ^{[1
]}

机构：

[1] Michigan State Univ, Dept Elect Engn, E Lansing, MI 48824 USA

来源：

2002 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I-IV, PROCEEDINGS | 2002年

关键词：

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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.

引用

页码：757 / 760

页数：4

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