淡江大學機構典藏:Item 987654321/95944
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    题名: Speech Classification Based on Fuzzy Adaptive Resonance Theory
    作者: Hsieh, Ching-Tang;Hsu, Chih-Hsu
    贡献者: 淡江大學電機工程學系
    关键词: Speech classification;Neuro-fuzzy system;Fuzzy ART
    日期: 2006-10
    上传时间: 2014-02-13 11:24:45 (UTC+8)
    摘要: This paper presents a neuro-fuzzy system to speech classification. We propose a multi-resolution feature extraction technique to deal with adaptive frame size. We utilize fuzzy adaptive resonance theory (FART) to cluster each frame. FART was an extension to ART, performs clustering of its inputs via unsupervised learning. ART describes a family of self-organizing neural networks, capable of clustering arbitrary sequences of input patterns into stable recognition codes. In our experiments, the TIMIT database is used and extracts features of each phoneme. The performance of speech classification is 88.66%, demonstrate the effectiveness of the proposed system is encouraging.
    關聯: Proceedings of 9th Joint Conference on Information Sciences，4頁
    显示于类别:[電機工程學系暨研究所] 會議論文

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