基於卷積神經網路之語音辨識

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姓名

楊恕先(Shu-Sian Yang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

基於卷積神經網路之語音辨識
(Speech Recognition by Using Convolutional Neural Network)

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摘要(中)

本論文在探討如何利用深度學習來進行語音辨識，而使用的辨識方法是先透過梅爾倒頻譜係數((Mel frequency cepstral coefficients, MFCCs)取得語音特徵參數，並輸入卷積神經網路(Convolutional Neural Network, CNN)進行語音辨識。
此法與傳統語音辨識方法最大不同是在於不需要建立聲學模型，以中文為例就省去建立大量聲母(consonant)、韻母(vowel)比對的時間。藉由透過MFCCs取得特徵參數後就可以透過卷積神經網路實現語音辨識，並且不會受到語言種類的限制。

摘要(英)

The thesis developed a speech recognition method for automatic speech recognition. In this speech recognition method, we obtained the speech feature parameters through Mel frequency cepstral coefficients and input a Convolutional Neural Network. The main difference between this Convolutional Neural Network speech recognition method and traditional speech recognition method is that it does not need to establish an acoustic model. For example, in Chinese, it saved a lot of time without establishing a large number of consonant and vowel models. After obtaining the speech feature parameters through the MFCCs, speech recognition is finished through Convolutional Neural Network.

關鍵字(中)

★ 語音辨識
★ 深度學習
★ 神經網路

關鍵字(英)

★ speech recognition
★ deep learning
★ neural network

論文目次

摘要 I
Abstract II
致謝辭 III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章緒論 1
1-1 研究動機 1
1-2 文獻回顧 2
1-3 章節架構 4
第二章語音辨識 5
2-1 前處理 6
第三章卷積神經網路 15
3-1 卷積神經網路架構 15
3-1-1 卷積層 16
3-1-2 池化層 18
3-1-3 全連接層 21
3-2 激活函數 22
3-3 權重更新 25
3-1-1 隨機梯度下降法(Stochastic gradient descent, SGD)
26
3-1-2 AdaGrad 27
3-1-3 Adam 28
第四章實驗結果 34
4-1 卷積神經網路深度對辨識的影響 37
4-2 激活函數對辨識的影響 39
4-3 權重更新對辨識的影響 41
4-4 神經網路優化方式 43
第五章結論與未來研究方向 44
5-1 結論 44
5-2 未來研究方向 46
參考文獻 47

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指導教授

莊堯棠

審核日期

2019-6-27

推文