臺灣博碩士論文加值系統

本論文主要目的在研究語音基頻偵測方法。由於基頻為存在於有聲語音中一個相當重要的特徵，在許多語音處理應用，諸如語音編解碼、語者辨識、發音校正及發聲器官病變診斷，都需要使用正確的基頻特徵。在過去數十年的研究，雖然提出了很多種基頻偵測方法，但在準確性及對雜訊的強健性仍有待改進。最近的基頻偵測技術為利用小波轉換特性來抽取語音信號的基頻，這也是本文的主要研究方向。

首先，我們模擬Chen和Wang所提出以小波轉換為基礎的基頻偵測方法，然後針對無法正確決定基頻的有聲語音提出改進的解決方法，稱之為方法一。在我們的研究中發覺對某些語音利用信號負值部分比一般傳統使用正值部分更能正確地求得基頻。此外，我們也改良了Shelby等人所提出的方法，稱之為方法二。

為了評估所提方法的準確性及強健性，將測試語音資料加入不同信號雜訊比的雜訊後，在各種情況下所得實驗結果與原始方法比較偵測錯誤率。從實驗數據觀察到方法一及方法二的基頻偵測錯誤率皆低於Chen和 Wang的方法與Shelby的方法。實驗結果驗證了我們對原始演算法所做的改進，對於正確地偵測語音信號基頻的確是有所助益。

The objective of this thesis is to study the pitch detection methods of speech signals. Pitch is a very important feature in voiced speech and it is widely used in speech processing applications such as speech coding, speaker recognition, phonetics, and voice disease diagnostics. Although there are many pitch detection methods have been proposed, the accuracy and robustness against noise are needed to further improve. Recently, the wavelet transform has been widely applied to the pitch detection of speech signals, so the wavelet-based pitch detection method is our research direction.

First, we simulate the Chen and Wang''s wavelet-based pitch detection method. We find that for some voiced speech if we detect the pitch from the negative portion of signal will be accurate than from the positive portion of signal. Besides, we simulate the method proposed by Shelby et al. It is the improved version of the first wavelet-based method proposed by Kadambe. In the thesis, the method modified from the Chen and Wang''s method is called method I, while the method improved from the Shelby''s method is called method II.

In order to evaluate the accuracy and robustness against noise for the pitch detection methods, we add white Gauss noise to our test data in different signal-to-noise ratio and compare the error rate of pitch detection with the original works. From the experimental results, the error rates of the method I and method II are lower than Chen and Wang''s and Shelby''s methods, respectively. It verifies that our modifications for the original methods are beneficial for the accurate pitch detection of speech signals.

Abstract (in Chinese) …………………………………i
Abstract (in English) …………………………………ii
Acknowledgment (in Chinese) ………………………iii
Contents………………………………………………iv
List of Abbreviations…………………………………vii
List of Figures ………………………………………viii
List of Tables …………………………………………x

Chapter 1 Introduction ……………………………1
1.1Motivation ……………………………………1
1.2Related Researches ……………………………3
1.3Thesis Organizations …………………………5

Chapter 2 Concepts of Speech Processing …………6
2.1Introduction ……………………………………6
2.2Model of Speech Production ……………………7
2.3Properties of Speech Signals ……………………9
2.4Short-time Analysis of Speech Signals …………15

Chapter 3 Review of Wavelet Theory …………………17
3.1Introduction ………………………………………17
3.2Properties of Wavelet Analysis ……………………18
3.3Introduction to Wavelet Transform …………………20
3.3.1Continuous Wavelet Transform ……………………20
3.3.2Discrete Wavelet Transform ………………………21
3.4Multi-Resolution Analysis …………………………25

Chapter 4 Pitch Detection Algorithms ………………27
4.1Introduction …………………………………27
4.2The Wavelet-based Methods …………………28
4.2.1The Chen''s Method…………………………28
4.2.2The Shelby''s Method………………………30
4.3Our Proposed Methods ………………………31
4.3.1Our Method I ………………………………31
4.3.2Our Method II………………………………33
4.4Survey of Some Methods ……………………35
4.4.1Auto-correlation Method……………………35
4.4.2AMDF Method ………………………………38
4.4.3Cepstrum Method……………………………40

Chapter 5 Experimental Results and Discussions …………42
5.1Introduction …………………………………………42
5.2Experimental Results …………………………………43
5.2.1Experiments for Chen''s Method ……………………44
5.2.2Experiments for Our Method I ………………………50
5.2.3Experiments for Shelby''s Method……………………57
5.2.4Experiments for Our Method II ………………………60
5.3Comparisons and Discussions …………………………65

Chapter 6 Conclusions and Future Studies …………………69
6.1Conclusions……………………………………………69
6.2Future Studies …………………………………………70
References…………………………………………………71
Vita ………………………………………………………73

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