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

关键词：

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.

引用

页码：229 / 234

页数：6

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