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Word Boundary Detection of Voice Signal Using Recurrent Fuzzy Associative Memory  

Ma Chang-Su ((주)핸디소프트)
Kim Gye-Young (숭실대학교 컴퓨터학부)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.9, 2004 , pp. 1171-1179 More about this Journal
Abstract
We describe word boundary detection that extracts the boundary between speech and non-speech. The proposed method uses two features. One is the normalized root mean square of speech signal, which is insensitive to white noises and represents temporal information. The other is the normalized met-frequency band energy of voice signal, which is frequency information of the signal. Our method detects word boundaries using a recurrent fuzzy associative memory(RFAM) that extends FAM by adding recurrent nodes. Hebbian learning method is employed to establish the degree of association between an input and output. An error back-propagation algorithm is used for teaming the weights between the consequent layer and the recurrent layer. To confirm the effectiveness, we applied the suggested system to voice data obtained from KAIST.
Keywords
word boundary detection; met-frequency; RFAM; hebbian learning;
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Times Cited By KSCI : 1  (Citation Analysis)
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