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Please use this identifier to cite or link to this item: https://dyhuir.dyhu.edu.tw/ir/handle/987654321/822

Title: 嵌入式語者辨認系統
Embedded Speaker Identification System
Authors: 許志旭
Keywords: 語者辨認;特徵抽取;小波轉換;多維矩形類神經模糊系統;speaker identification;feature extraction;Wavelet transform;Hyper rectangular neuro-fuzzy system
Date: 2008
Issue Date: 2012-01-02T04:13:56Z
Abstract: 本研究計畫主要的工作為語者辨識,先進行快速的非特定文字語者辨認系統,在辨識模型參數的數量上有嚴格的限制,因此本計畫針對這些限制提出有效的解決方案。在語音特徵方面萃取語音訊號的小波轉換參數;在辨識器方面選用多維矩形類神經模糊系統。多維矩形類神經模糊系統(Hyper Rectangular Neuro-Fuzzy System, HRNFS)具有複雜決策邊界的辨識器,藉由誤差的傳遞修改權重值,並採用監督式決定導向學習 (Supervised Decision-Directed Learning, SDDL) 演算法訓練多維矩形類神經模糊系統,很容易以If-then的形式萃取分類規則。測試語料以德州儀器與麻省理工學院(TIMIT)共同開發的語音資料庫為主,實驗結果顯示本系統有很好的辨識效果。
The objective of this project is to find an effective middleware of speaker identification. It is to search effective methods of text-independent speaker identification with the constraints on the limited parameters of the model. We extract a set of features of each speech frame from wavelet transform and use hyper rectangular neuro-fuzzy system (HRNFS) as the classifier. HRNFS is a classifier with complicated decision boundary. The weighted values were tuned by propagation errors, and the HRNFS was trained by supervised decision-directed learning (SDDL) algorithm. It is easy to represent rules by If-then. In our experiments, the speech database is the Texas Instrument / Massachusetts Institute of Technology (TIMIT) acoustic-phonetic corpus of read speech. The effectiveness of the proposed system is confirmed by the experimental results.
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