N-gram Language Models in JLASER Neural Network Speech Recognizer

被引：0

作者：

Konopik, Miloslav ^{[1
]}

Habernal, Ivan ^{[1
]}

Brychcin, Tomas ^{[1
]}

机构：

[1] Univ W Bohemia, Dept Comp Sci & Engn, Plzen 30614, Czech Republic

来源：

2010 INTERNATIONAL CONFERENCE ON APPLIED ELECTRONICS | 2010年

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In our recent research we have discovered that neural networks can be more efficient in speech recognition than the state of the art approach based on Gaussian mixtures. This statement is valid only for small corpora, however, many applications do not require a huge recognition vocabulary. In this article we describe our speech recognizer - called JLASER - based on neural networks. We also show the effect of n-gram language models applied to the JLASER recognizer.

引用

页码：167 / 170

页数：4

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