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研究生： 官大文
Kuan, Ta-Wen
論文名稱： 語者識別之超大型積體電路架構設計及其在智慧玄關應用之研究與實現
The Research and Realization of Speaker Recognition on VLSI Architecture Design and Intelligent Porch Application
指導教授： 王駿發
Wang, Jhing-Fa
學位類別： 博士
Doctor
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2012
畢業學年度： 100
語文別： 英文
論文頁數： 98
中文關鍵詞： 語者識別 、語者驗證 、支援向量機 、序列最小最佳化 、超大型積體電路架構設計 、軟硬體共同設計 、智慧玄關 、生物特徵融合 、多模生物辨識
外文關鍵詞： Speaker Recognition, Support Vector Machine, Sequential Minimal Optimization, VLSI Design, SoC Design, Hardware/Software Co-design, Smart Home, Intelligent Porch System, Multi-modal Bio-metric System, Audio and Video experts, Embedded system
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    • 隨著智慧生活的概念逐漸普及，智慧生活中的行動智慧裝置(如智慧型手機，PAD及筆記型電腦等等)，體積愈來愈小，功能愈來愈強，讓使用者生活便捷，成為生活中不可或缺的一部分；同時智慧生活中的智慧家庭，各項智慧生活家電和門禁安全系統，讓使用者居家備感安全舒適及便捷。語者識別，扮演著行動智慧裝置擁有者的身分確認；及家庭智慧玄關系統，家庭成員身分驗證及識別的重要角色。本論文主要在探討語者識別應用於超大型積體電路架構設計及智慧玄關應用之研究與實現。
    語者識別，包含語者辨識及語者驗證。語者辨識是屬於封閉集的有限語者的身分識別，識別過程中分數最高者，可被判別為目標語者。語者驗證是開放集的語者的身分驗證，在已知語者身分條件下，驗證語者聲學特性的是否高於特定門檻值的，以判別語者身分的信心程度。為了增進語者識別在實務應用的程度，本論文同時探討語者識別結合特定語句及非特定語句的語音辨識，以提高語者識別的正確率。
    在研究過程中，我們首先在實驗室天花板，分散架設六支收音麥克風，建立一個類似居家，無所不在遠距的收音環境；並在個人電腦平台建立以支援向量基SVM分類器為主之語者辨識軟體模擬系統，以測試遠距聲學環境之語者辨識效能。接下來語者識別分兩個方向研究，一為硬體部分研究與實現，另一為奇美體驗屋的智慧玄關研究與實現。硬體部分包含實現於ARM板的嵌入式系統單晶片架構設計、FPGA板的超大型積體電路架構設計及ARM+FPGA的軟硬體共同設計架構。智慧玄關部分，語者識別結合特定語句之語音辨識，以提高語者識別的可用性。進一步，利用生物特徵之融合方法，除原有語者識別及語音辨識外，同時結合遠距人臉及身高影像辨識，以提高多模生物辨識系統應用於家庭智慧玄關身分辨識的強健性。
    語者識別，可分為語者模型訓練及語者辨識分類二階段。為加速訓練語者模型，我們使用”支援向量機”之”序列最小最佳化”方法來實現。在語者辨識分類部分，採用一對一多類別方法來提高辨識率。為進一步提高語者模型訓練的效能，我們將”支援向量機”之”序列最小最佳化”方法，實現於超大型積體電路架構設計。實驗結果顯示，以超大型積體電路架構設計之語者模型訓練，訓練效能有顯著提升，同時語者辨識率幾乎與個人電腦軟體模擬結果相近。

    In smart life, the development of smart portable devices and smart home appliances, have attracted the researchers to improve in their tiny size, high performance, interactive application, and powerful functionality. The speaker recognition plays the important role for the owner recognition in mobile device, and the enrollment authentication at smart home.
    In this dissertation, we explore the speaker recognition in two fields, that is, the hardware implementation and the smart home application. In hardware realization, multiple platforms, such as ARM platform, FPGA platform and ARM+FPGA platform, are adopted to explore the speaker recognition, and realize into the embedded SoC system, VLSI architecture design and Hardware/Software co-design. In smart home, the speaker recognition is investigated in intelligent porch system to attain the nature way for home user authentication and to interact smartly with home appliances. However, the adverse and mismatch conditions influence the speaker expert, therefore, the speaker expert is proposed to fuse with other human cues, such as, speech expert, face expert and height detector, to reach the multi-modal and biometric recognition system for smart home.
    In general, the speaker recognition can be categorized in two modalities, i.e. speaker identification and speaker verification. The speaker identification scores and determines the target speaker’s identity from unknown speaker in a close set of trained models, whereas the speaker verification verifies the claimed voice with corresponding claimed identity, through a confident threshold to determine the target speaker, such a task can be regarded as an open set.
    Two critical phases are commonly addressed in speaker recognition, that is, model training and speaker recognition. Generally, the model training is time-consuming particularly in mobile device. This motives us to examine the training phase in hardware implementation to accelerate the training performance. In this dissertation, the Support Vector Machine (SVM) is exhibited for the speaker model training and classification, and the Sequential Minimum Optimization (SMO) algorithm in SVM, is used to accelerate the speaker model training. In order to realize the complex SMO algorithm on multiple hardware platforms, the SMO algorithm is analyzed and modified prior to the feasible steps and blocks, and then realized on several hardware platforms. The experimental results show that the VLSI design of SMO algorithm indeed accelerates the training speed, and the accuracy in speaker identification has no big difference compared with software simulation.

    CONTENTS vi LIST OF TABLES ix LIST OF FIGURES x Chapter 1 Introduction 1 1.1 Speaker Recognition 1 1.2 Support Vector Machine 2 1.2.1 Review of Support Vector Machine 2 1.2.2 Review of Sequential Minimum Optimization 4 1.2.3 Review of SVM-based Speaker Recognition 6 1.2.4 Review of VLSI Design on Sequential Minimum Optimization Algorithm 6 1.2.5 Review of Multi-modal Biometric Fusion Recognition 7 1.3 Motivation 9 1.4 Purposes and Research Approaches 10 1.5 Organization of this Dissertation 12 Chapter 2 Software Simulation in Ubiquitous and Text-Independent SVM-based Speaker Recognition 13 2.1 System Framework of Ubiquitous and SVM-based Speaker Recognition 13 2.1.1 System Framework 13 2.1.2 MFCC and LPCC Feature Extraction Methods 15 2.1.3 Linear and Non-linear kernel functions in SVM 16 2.2 Experimental Results 17 2.3 Summary 18 Chapter 3 SoC Design on SVM-based Speaker Recognition and VLSI Design on Sequential Minimum Optimization Algorithm 19 3.1 Introduction 19 3.2 Dynamic Fixed-Point Arithmetic Design on Embedded Speaker Recognition System 20 3.2.1 Dynamic Fixed-Point Arithmetic Design and Model Analysis 20 3.2.2 Experimental Results 23 3.2.3 Summary 24 3.3 VLSI Design on Sequential Minimum Optimization Algorithm 24 3.3.1 Framework Overview 24 3.3.2 VLSI Design on SMO Algorithm 25 A. Step 1 - Preprocess Module 27 B. Step 2 - Lagrange Multiplier Updating Module 31 C. Step 3 - SVM Parameter Updating Module 32 3.3.3 Experimental Results 39 A. Software Simulation 39 B. FPGA Implementation and Performance Evaluation 40 3.3.4 Summary 44 3.4 An Efficient HW/SW Co-design of an SVM-Based Speaker Recognition Engine 44 3.4.1 Introduction 44 3.4.2 System Framework 45 A. Overview 45 B. Software-based Preprocessing Extraction Block 47 C. Software-based Effective-feature Block 47 1) Effective Data Selection 48 2) Data Packed/unpacked Communication 49 D. Hardware-based Accelerator Block 50 E. Software-based Decision Block 51 3.4.3 HW/SW co-realization of Speaker Recognition Engine 52 A. System Architecture 52 B. HW/SW Co-optimization 53 C. HW/SW Co-verification 54 3.4.4 Experimental Results 54 A. Hardware Implementation 55 B. Performance Evaluation and Comparison 55 3.4.5 Summary 57 Chapter 4 A New Hybrid and Dynamic Fusion of Multiple Experts for Intelligent Porch System 58 4.1 Introduction 58 4.2 Audio-Visual expert and fusion approach 59 4.2.1 Speaker and Speech Expert 59 4.2.2 Distant face expert and height detector 60 4.2.3 HPTD-fusion in outdoor authentication 63 4.2.4 PMDW-fusion in indoor interaction 65 4.3 Intelligent and interactive poach system 67 4.3.1 System Framework and Functionality 67 4.3.2 Outdoor authentication and Indoor automation 71 A. Text-to-speech-reminder (TTSR): 71 B. Voice-interacting-reminder (VIR) 72 C. Speech-trigger-door-lock-reminder (STDLR) 72 4.3.3 Outdoor and indoor prototyping 73 4.4 Experimental results 75 4.4.1 Outdoor HPTD-fusion approach 75 4.4.2 Indoor PMDW-fusion approach 76 4.5 Summary 78 Chapter 5 Conclusions 80 5.1 Principal Contributions 80 5.2 Future Directions 81 5.2.1 Reconfigurable Architecture Design of SVM Learning Core on Sequential Minimum Optimization 81 5.2.2 Multimodal Biometric Recognition for Smart Home Robot 82 BIBLIOGRAPHY 83 Publication List 96 Vita……… 98

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