Robust speech recognition using time boundary detection

被引：1

作者：

Mohajer, K ^{[1
]}

Hu, ZM ^{[1
]}

机构：

[1] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA

来源：

MULTISENSOR, MULTISOURCE INFORMATION FUSION: ARCHITECTURES, ALGORITHMS, AND APPLICATIONS 2003 | 2003年 / 5099卷

关键词：

speech recognition; Hidden Markov Models; time boundary detection; speech segmentation;

D O I：

10.1117/12.488199

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper explores the benefits of including time boundary information in Hidden Markov Model based speech recognition systems. Traditional systems normally feed the parameterized data into the HMM recognizer, which result in relatively complicated models and computationally expensive search steps. We propose a few methods of detecting time boundaries prior to parameterization, and present a novel way of including this additional information in the recognizer. The result is significant simplification in the model prototypes, higher accuracy and faster performance.

引用

页码：335 / 343

页数：9

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