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Proceedings Paper

Robust speech recognition using time boundary detection
Author(s): Keyvan Mohajer; Zhong-Min Hu
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Paper Abstract

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.

Paper Details

Date Published: 1 April 2003
PDF: 9 pages
Proc. SPIE 5099, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003, (1 April 2003); doi: 10.1117/12.488199
Show Author Affiliations
Keyvan Mohajer, Stanford Univ. (United States)
Zhong-Min Hu, Stanford Univ. (United States)


Published in SPIE Proceedings Vol. 5099:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003
Belur V. Dasarathy, Editor(s)

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