本論文以實現一個強健性的語音辨識系統於FPGA平台上為主要目標。為了加快語音辨識處理的速度，以適合於速度較慢的嵌入式平台上，採用了整數快速傅立葉轉換取代浮點數快速傅立葉轉換，在辨識率些微下降下，大幅提高了快速傅立葉轉換的運算速度。對於含雜訊的語音，採用了黃鍔院士所提出的經驗模態分解法(Empirical Mode Decomposition, EMD)將含雜訊的語音訊號分離成多組本質模態函式(Intrinsic Mode Function, IMF)，針對不同本質模態函式做語音辨識上之實驗，以期能找出本質模態函式與語音雜訊間之關係，並以實數型基因演算法找出本質模態函式之權重，將分離出之本質模態函式依權重還原成語音後，能降低雜訊之干擾，提升語音辨識系統抗雜訊之能力。

In this thesis, we propose a robust speech recognition system constructed by utilizing HMM; the system utilizes Integer FFT to improve the computing performance and had been implemented in the FPGA embedded system. In the embedded system, the computing speed is not as fast as personal computer which would cause the speech recognition to consume more computing and power; in further, the requirement of real-time recognition would not be met. In the proposed method, we utilize Integer FFT to replace Float FFT and the experimental result shows that much computing time could be reduced while only a little recognition accuracy is lost. In addition, we also utilize Empirical Mode Decomposition to divide speech signals into multiple Intrinsic Mode Functions, and analyze the correlation between speech signal and noise in each Intrinsic Mode Function. Finally, we utilize Genetic Algorithm to find the optimal composing weight, and then, use it to reassemble the multiple Intrinsic Mode Functions to the speech signal for speech recognition.

第一章 緒論 1
1.1 研究動機 1
1.2 研究方法 2
第二章 語音的前置處理 3
2.1 語音取樣 3
2.2 音框 4
2.3 端點偵測 5
2.4 預強調 6
2.5 漢明窗 6
2.6 整數快速傅立葉轉換 7
2.7 梅爾頻率倒頻譜參數 11
第三章 隱藏式馬可夫模型 14
3.1 向量量化 14
3.2 隱藏式馬可夫模型 15
3.3 計算模型機率 16
3.4 訓練模型參數 17
3.5 辨識 19
第四章 EMD訊號分解與語音辨識 21
4.1 瞬時頻率 21
4.2 本質模態函式 22
4.3 三次仿樣函式 23
4.4 經驗模態分解法 26
4.5 經驗模態分解法與語音辨識 28
第五章 基因演算法 32
5.1 決定變數和計價函式 33
5.2 實數型和離散型參數 34
5.3 天擇 36
5.4 選擇 37
5.5 交配 38
5.6 突變 39
第六章 實驗環境與結果 40
6.1 實驗參數設定與硬體 40
6.2 實驗方法 43
6.3 實驗結果 46
第七章 結論與展望 52
7.1 結論 52
7.2 未來與展望 52
參考文獻 54

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