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中興大學電機系資訊智慧實驗室

LWE. 的高抗雜訊能力. ZCR, MFCC , time noise. LWE , ZCR. Speech 11kHz. RSONFIN. SVM. SVM-SOFN. To add noise. ILN. To regulate outputs by ROC curve. Frame block. FEC MSC OVER NDS. Speech recognition by HRNFN. 以智慧學習網路執行變動噪音環境下的語音偵測. 研究 生 :鄭 君 楠 指導教授 : 莊家峰. 中興大學電機系資訊智慧實驗室 .

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中興大學電機系資訊智慧實驗室

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  1. LWE 的高抗雜訊能力 ZCR, MFCC , time noise LWE , ZCR Speech 11kHz RSONFIN SVM SVM-SOFN To add noise ILN To regulate outputs by ROC curve Frame block FEC MSC OVER NDS Speech recognition by HRNFN 以智慧學習網路執行變動噪音環境下的語音偵測 研究 生 :鄭 君 楠 指導教授: 莊家峰 中興大學電機系資訊智慧實驗室 本論文提出利用LWE-based參數為基礎的智慧型學習網路偵測器於語音切割。LWE-based參數只需LWE跟ZCR兩參數即可偵測出變動噪音環境下的語音訊號，且擁有不錯的抗雜訊能力。在偵測器部份，我們分別使用了三種網路，遞迴類神經模糊網路(RSONFIN)，向量支持機(SVM) ，以向量支持機輔助之自我組織模糊網路(SVM-SOFN) 。結論為所提出的LWE參數有較好的表現。而三個網路的特點分別是: RSONFIN較少的參數量，SVM具有非常容易訓練的特點，SVM-SOFN有較少的參數量及容易訓練的特點。 LWE方程式 語音流程圖 白雜訊的辨識結果 高變化雜訊的切割實例

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