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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/46169

    Title: A neuro-fuzzy approach to speech recognition without time alignment
    Authors: 蘇木春;Su, Mu-chun;Hsieh, Ching-tang;Chin, Chieh-ching
    Contributors: 淡江大學電機工程學系
    Keywords: Speech recognition;Neural networks;Pattern recognition;Expert system;Membership function
    Date: 1998-08-16
    Issue Date: 2010-03-26 21:07:54 (UTC+8)
    Publisher: Medwell Journals
    Abstract: Several successful approaches to speech recognition have been proposed. Most of them involve time alignment which requires substantial computation and considerable memory storage. In this paper, we present a neuro-fuzzy approach to speech recognition without time alignment. This approach is a powerful method for selecting reference templates; therefore, considerable memory storage is alleviated. In addition, it greatly reduces substantial computation in the matching process because it obviates time alignment. Base on this approach, a Mandarin speech recognition system without time alignment is implemented. Two databases were utilized for verifying its performance. An encouraging experimental result confirms the effectiveness of the proposed neuro-fuzzy approach to speech recognition without time alignment.
    Relation: Fuzzy Sets and Systems 98(1), pp.33-41
    DOI: 10.1016/S0165-0114(96)00375-2
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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