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San Jose, California
Nov. 2, 2007 to Nov. 4, 2007
ISBN: 0-7695-3032-X
pp: 506
ABSTRACT
Speech endpoint detection is an important step in the field of speech analysis, speech synthesis and speech recognition. This paper proposed an endpoint detection algorithm, which used amplitude entropy, spectral entropy and frame energy as feature parameters and utilized RBF neural network as a feature classification system. 170 sentences are used as testing data to detect speech endpoint, which length is from 4 second to 7 second. The experiments show that the testing results using RBF neural network are better than that using entropy alone or BP neural network based on entropy. Keywords--amplitude entropy, neural network, speech endpoint detection, spectral entropy
CITATION
Xueying Zhang, Gaoyun Li, Feng Qiao, "A Speech Endpoint Detection Algorithm Based on Entropy and RBF Neural Network", GRC, 2007, 2013 IEEE International Conference on Granular Computing (GrC), 2013 IEEE International Conference on Granular Computing (GrC) 2007, pp. 506, doi:10.1109/GrC.2007.95
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