文句不相關語者驗證使用支援向量機｜國立臺灣科技大學博碩士論文系統

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研究生：	旭法 Sivabalan - Adinarayanan
論文名稱：	文句不相關語者驗證使用支援向量機 Text-Independent Speaker Verification Using Support Vector Machine
指導教授：	洪西進 Shi-Jinn Horng
口試委員:	王有禮 Yue-Li Wang 梅興 Hsing Mei 王振興 Jeen-Shing Wang 楊昌彪 Chang-Biau Yang
學位類別：	碩士 Master
系所名稱：	電資學院 - 資訊工程系 Department of Computer Science and Information Engineering
論文出版年：	2005
畢業學年度：	93
語文別：	英文
論文頁數：	83
中文關鍵詞：	梅爾倒頻譜參數、支援向量機、語者驗證
外文關鍵詞：	Support Vector Machine, Speaker Verification, MFCC
相關次數：	點閱：206 下載：2
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系統實作中，以梅爾倒頻譜參數(Mel-Frequency Cepstral Coefficients, MFCCs)做為語者特徵，結合支援向量機(Support Vector Machine)建立語者相依模型。

This dissertation aims to explore the technology of speaker recognition,
specifically by researching the technique in current state-of-the-art systems. Current
state-of-the-art speaker verification systems are based on discriminatively trained
generative models. In these systems, discrimination is achieved with the linear
function. We studied the use of support vector machines (SVMs) for text
independent speaker verification. Two main approaches were considered. The first is
approach using linear SVMs. The second approach is an utterance based approach
using kernels SVMs. State-of-the-art speaker verification systems rely on generative
models to recognize speakers. It is a curious result since discriminative approaches
for classification should in theory be better than generative ones since the former are
optimized to minimize the classification error rate explicitly compared to the latter.
The polynomial kernel and radial basis function kernel are widely used for
speaker verification task. We examine the properties of the linear SSVMs in
comparison. By doing so, we will be able to study or adopt a simpler system with
faster execution time which would yield to high or close performance in term of
accuracy with the current kernel methods. The approach using linear SVMs is to
study the method efficiency in simplicity and time consumption in reducing the error
rate. This is in order to overcome the difficulties arising from an application of
complex kernel SVMs to speaker verification. We begin with an investigation into
the similar kernel functions like polynomial and RBF kernels. This technique were
tested on one of the top ten database named YOHO database and then evaluated on
the more difficult custom-build text-independent database. This separation of the development from the evaluation is important to ensure that the methods are general
and that the classifiers have not been tuned to one particular database.
Experimentally the linear SVMs benefits, by not only out perform current
state-of-the-art classifiers on the YOHO text-independent speaker verification
database but even with the kernel functions yielding to a close result and faster
execution time. This thesis reports equal error rates on the YOHO database that are
1.81% of equal error rate and 0.65% of equal error rate with our ownbuild textindependent
database

Abstract i
Acknowledgements iii
List of Figures vi
List of Tables vi
1 INTRODUCTION
1.1 Introduction 2
1.2 Goals and Motivation 5
1.3 Overview 6
2 BACKGROUND
2.1 Acoustic Models 9
2.2 Speech Production 11
2.3 Previous Work 14
2.4 Applications 15
2.5 Pros and Cons of Speaker Recognition 18
2.6 Elementary Concepts and Terminology 19
2.6.1 Speaker Identification 20
2.6.2 Speaker Verification 21
2.7 Text-Dependent 22
2.8 Text-Independent 23
3 Feature Extraction (MFCC)
3.1 Introduction 25
3.2 Mel-Frequency Ceptrum Coefficients Processor (MFCC) 28
3.2.1 Frame Blocking 29
3.2.2 Windowing 30
3.2.3 Fast Fourier Transform (FFT) 31
3.2.4 Mel-Frequency Wrapping 32
3.2.5 Ceptrum 34
4 Vector Quantization (VQ)
4.1 Introduction 36
4.2 Vector Quantization 37
5 Support Vector Machine (SVM)
5.1 Speaker Modeling 44
5.2 Conventional Support Vector Machine 47
5.3 Variational Support Vector Machine 54
6 Experiments & Results
6.1 Error Reporting 58
6.2 Corpora 63
6.2.1 Custom-Build Database 64
6.2.1.1 Custom-Build Text-Dependent Database 64
6.2.1.1.1 Experimental Procedure 64
6.2.1.2 Custom-Build Text-Independent Database 67
6.2.1.2.1 Experimental Procedure 67
6.2.2 The YOHO Voice Verification Corpus 70
6.2.2.1 Experimental Procedure 71
7 Conclusion
7.1 Conclusion 76
REFERENCES

List of Tables Page
Table 3.1: A summary of some common windowing functions 31
Table 6.1: Speaker Verification Error Rate 74
List of Figures Page
Figure 1.1: Speaker Verification Flow 5
Figure 1.2: Training and Testing Flow 5
Figure 2.1: Schematic and circuit model of the vocal tract [4] 10
Figure 2.2: Acoustic tube model of speech production 12
Figure 2.3: Speech production mechanism [8] 13
Figure 2.4: Applying speaker recognition in speech recognition 17
Figure 2.5: Areas of voice (speaker) recognition 19
Figure 2.6: Basic structure of speaker identification system 20
Figure 2.7: Basic structure of speaker verification system 21
Figure 3.1: An Example of speech signal 25
Figure 3.2: Frame-based analysis 27
Figure 3.3: Mel scale filterbank 28
Figure 3.4: Block diagram of the MFCC processor 29
Figure 3.5: How the parameter N and M are utilized in the frame blocker 30
Figure 3.6: Common time windows, with durations normalized to unity 30
Figure 3.7: An example of Mel-spaced filterbank 33
Figure 4.1: Conceptual diagram illustrating vector quantization codebook
formation. One speaker can be discriminated from another based of
the location of centroids [34] 37
Figure 4.2: The process of VQ codebook generation; the features are shown
by blue dots, the group boundary in green and the centroids are in red 41
Figure 4.3: Flow of the binary split codebook generation algorithm. [5] 42
Figure 5.1: Training data is perfectly linearly separated by multiple
hyperplanes in R2 49
Figure 5.2: A hyperplane is found by the SVM in R2. The support vectors are
circled 49
Figure 5.3: The support vector (circles points) of a soft margin SVM in R2 52
Figure 5.4: An example of kernel mapping 53
Figure 6.1: Imposter Scores Distribution and FAR 59
Figure 6.2: Client Scores Distribution and FRR 60
Figure 6.3: Overlapping of distribution of the client and the imposter,
FAR and FRR 60
Figure 6.4: Reporting classifier performance on ROC and DET curves
(a): An ROC curve illustrates the trade-off between probability of false
acceptances (horizontal axis) against the true acceptance probability
or one minus the false rejection probability (vertical axis) 62
(b): The DET curves corresponding to the ROCs shown in (a). The false
acceptance probability (horizontal axis) is plotted against the
false rejection probability (vertical axis). The conversion from
probabilities to the normal deviate scale is shown in (c) 63
(c): The normal deviate is found by computing the percentage area
under the normal distribution 63
Figure 6.5: Text-Dependent DET Plot (100 Speakers) 66
Figure 6.6: FAR & FRR Versus Threshold (100 Speakers) 66
Figure 6.7: Text-Independent DET Plot (30 Speakers) 69
Figure 6.8: FAR & FRR Versus Threshold (30 Speakers) 69
Figure 6.9: Text-Independent DET Plot (138 Speakers) 73
Figure 6.10: FAR & FRR Versus Threshold (138 Speakers) 73

                                

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