博碩士論文 102521070 詳細資訊




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姓名 唐曲亮(Chu-Liang Tang)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 改良式梅爾倒頻譜係數混合多種語音特徵之研究
(Improved Mel Frequency Cepstral Coefficients Combined with Multiple Speech Features)
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摘要(中) 本篇論文主要研究的主題是語音辨識系統中的特徵值擷取以及特徵參數補償的部分,前者目的是將不同的特徵值做合併,其中將線性預估倒頻譜係數與梅爾倒頻譜係數結合的效果是最佳的,本論文使用高斯型的梅爾濾波器組來取代原本梅爾倒頻譜係數中的三角濾波器組,而經過實驗證實,將線性預估倒頻譜係數與梅爾倒頻譜係數以1:1的方式做合併效果是最好的,除了將特徵參數做合併之外,本論文還利用倒頻譜平均值與變異數正規化法來補償倒頻譜係數並提升整體系統的辨識效果。
摘要(英) This thesis studies the speech feature extracting and feature compensation in speech recognition. Several speech features are selected for combinations. The best one is cascading Linear Prediction Cepstral Coefficients (LPCC) and Mel-Frequency Cepstral Coefficient (MFCC). The MFCCs used here are obtained by utilizing a Gaussian Mel-Frequency band instead of using a triangular filter bank. And by experiments, it is found that the best combination ratio of LPCC and MFCC is 1:1. The thesis also showed that further improved performance is possible if Cepstral Mean and Variance Normalization (CMVN) is added.
關鍵字(中) ★ 語音辨識
★ 特徵合併
★ 梅爾倒頻譜係數
★ 關鍵詞萃取
關鍵字(英) ★ speech recognition
★ feature combination
★ MFCC
★ keyword spotting
論文目次 摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VI
表目錄 VII
第一章 緒論 1
1.1 研究動機 1
1.2 研究目標 1
1.3 文獻回顧 2
1.4 章節摘要 4
第二章 語音訊號處理 5
2.1 特徵參數擷取[17] 5
2.2 隱藏式馬可夫模型 10
2.3 聲學模型 12
2.4 模型訓練 13
2.4.1 狀態排列(State Arrangement) 14
2.4.2 初始化(Initial Model) 14
2.4.3 維特比演算法(Viterbi Algorithm) 15
2.4.4 參數調適、機率估算 16
第三章 特徵參數的改良以及合併 17
3.1 LPCC 17
3.2 MFCC 20
3.3 PLPCC 23
3.4 多種梅爾濾波器組 25
3.5 合併特徵參數的方法 29
第四章 關鍵字萃取 30
4.1 關鍵詞萃取架構 30
4.2 辨識流程 32
第五章 實驗結果 35
5.1 實驗環境 35
5.2 特徵補償的實驗 37
5.3 單一特徵參數實驗 40
5.4合併特徵參數實驗 41
5.5 維度分配實驗 44
第六章 結論與未來展望 46
6.1 結論 46
6.2 未來展望 47
參考文獻 48
附錄 53
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指導教授 莊堯棠(Yau-tarng Juang) 審核日期 2015-7-13
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