• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.449-456
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• 2004

This paper proposes a novel recognition model, a recurrent fuzzy associative memory(RFAM), for recognizing time-series patterns contained an ambiguity. RFAM is basically extended from FAM(Fuzzy Associative memory) by adding a recurrent layer which can be used to deal with sequential input patterns and to characterize their temporal relations. RFAM provides a Hebbian-style learning method which establishes the degree of association between input and output. The error back-propagation algorithm is also adopted to train the weights of the recurrent layer of RFAM. To evaluate the performance of the proposed model, we applied it to a word boundary detection problem of speech signal.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.235-237
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• 2003

본 논문에서는 음성인식을 위한 전처리 단계로 음성인식의 대상을 찾아내는 음성경계 추출에 대하여 기술한다. 음성경계 추출을 위한 특징 벡터로는 시간 정보인 RMS와 주파수 정보인 MFBE를 사용한다. 사용하는 알고리즘은 학습을 통해 규칙을 생성하는 퍼지연상기억장치에 음성의 시간 정보를 적용하기 위해 순환노드를 추가한 새로운 형태의 순환 퍼지연상기억장치를 제안한다.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1171-1179
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• 2004

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.