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標題: 結合遞迴模糊網路與動態時間校準執行中文辭組辨識
Combination of Recurrent Fuzzy Network and Dynamic Time Warping for Mandarin Phrase Recognition
作者: 賴俊龍
Lai, Chun-Lung
關鍵字: Mandarin Phrase Recognition;中文辭組辨識
出版社: 電機工程學系所
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本論文提出國字辭組的辨識利用DTW的原理基於SRNFN預測誤差,這樣的方法稱為DTW-SRNFN. SRNFN的遞迴迴歸的特性使其更適合處理時序上的語音訊號. 每一個辭組所包含的字彙均為單音節.SRNFN的訓練是對於單字去做訓練.n個SRNFN去模組化n個字彙,其中每一個SRNFN接收現在的音框特徵並且預估下ㄧ刻的音框特徵進而模組化字彙.每一個SRNFN的預測誤差被用來作為辨識的準則.在m個辭組的辨識中,對於辭組每一個音框的預測誤差利用各個已訓練的SRNFN來計算,之後形成了一個誤差矩陣.基於誤差矩陣,DTW被用來尋找一個最佳的路徑,映射在最佳被匹配的SRNFN對於每一個辭組的輸入音框中.每個辭組的累積誤差依據最佳的路徑被計算出來並且最小的累積誤差為辨識的結果.為了証實DTW-SRNFN的性能,實驗中30類辭組的辨識可分為57類單音的字彙來做處理.此外,對於處理加有不同程度的雜訊的語音辨識SRNFN輸入加有雜訊的特徵值作訓練.DTW-SRNFN的性能被用來與HMM做比較,結果顯示DTW-SRNFN可達到較HMM高的辨識率在乾淨與加有雜訊的環境中.最後,對於DTW-SRNFN與基於小波的強健語音切音方法的及時辨識已實現在基於PC的系統上.

This paper proposes Mandarin phrases recognition by Dynamic Time Warping (DTW) of Singleton-type Recurrent Neural Fuzzy Networks (SRNFN) prediction errors, and the method is called DTW-SRNFN. The recurrent property of SRNFN makes them suitable for processing temporal speech patterns. A Mandarin phrase comprises words each of which is monosyllabic. Training of SRNFN is based on the unit of words. There are SRNFN for modeling words, where each SRNFN receives the current frame feature and predicts the next one of its modeling word. The prediction error of each SRNFN is used as recognition criterion. In phrases recognition, the prediction errors of each trained SRNFN for each phrase frame is computed, resulting in an error matrix. Based on the error matrix, DTW is used to find an optimal path that maps the input frames to a best matched SRNFN (word) for each of the phrases. The accumulated error of each phrase is computed from its optimal path and the minimum one is the classified result. To verify the performance of DTW-SRNFN, experiments on recognition of 30 Mandarin phrases comprising 57 words are conducted. In addition, training of SRNFN with noisy features for noisy speech recognition with different types of noises is also conducted. Performance of DTW-SRNFN is compared with Hidden Markov Models (HMM). The results show that DTW-SRNFN achieves higher recognition rates than HMM in both clean and noisy environments. Finally, a PC-based system is set up for real-time implementation of the proposed DTW-SRNFN together with a wavelet-based robust speech detection method.
其他識別: U0005-0607200715081000
Appears in Collections:電機工程學系所

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