Developing HMM-based recognizers with ESMERALDA

被引:0
作者
Fink, GA [1 ]
机构
[1] Univ Bielefeld, Fac Technol, D-33501 Bielefeld, Germany
来源
TEXT, SPEECH AND DIALOGUE | 1999年 / 1692卷
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D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
ESMERALDA is an integrated environment for the development of speech recognition systems. It provides a powerful selection of methods for building statistical models together with an efficient incremental recognizer. In this paper the approaches adopted for estimating mixture densities, Hidden Markov Models, and n-gram language models are described as well as the algorithms applied during recognition. Evaluation results on a speaker independent spontaneous speech recognition task demonstrate the capabilities of ESMERALDA.
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页码:229 / 234
页数:6
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