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研究生：	莊則敬 Chuang, Ze-Jing
論文名稱：	針對聽語障人士之語音及手語處理技術之研究 A Study on Speech and Sign Language Processing for Speech/Hearing Impaired
指導教授：	吳宗憲 Wu, Chung-Hsien
學位類別：	博士 Doctor
系所名稱：	電機資訊學院 - 資訊工程學系 Department of Computer Science and Information Engineering
論文出版年：	2007
畢業學年度：	95
語文別：	中文
論文頁數：	92
中文關鍵詞：	多媒體展現技術、電腦輔助系統、聽語障人士、發音學習和矯正、虛擬人物合成、手語影片合成
外文關鍵詞：	communication skill learning, pronunciation learning and rectification, multimedia representation, deaf, 3D virtual character, sign language synthesis
相關次數：	點閱：83 下載：4
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　　由於近來即時資料庫存取及多媒體展現技術的快速發展，電腦輔助系統已經成為一個強化教育品質的有利工具，特別是在聽語障學童的溝通輔助學習上。許多的溝通輔助技術已經被應用在提高聽語障學童的溝通輔助技能學習上。由於聽語障人士難以自然語言溝通，因此大多數溝通輔助系統都是著眼在其他替代性溝通技能上的強化與提升，如手語、唇語、或其他整合溝通技巧。不過，這些系
統尚有若干缺點。以自然語言文法的學習為例，單獨使用文字及圖像按鍵為輸出入介面的手語學習系統通常是無效的。由於這些系統只能提供片段語言單元，如單字、片語、或子句，學習者無法由系統提供的介面理解自然語言中完整的文法架構。本論文提出一個針對聽語障人士設計之整合溝通技巧學習、訓練、及教學的架構。本論文同時描述一套依此架構建立的溝通輔助系統：TaiSign。TaiSign 為一溝通輔助系統，目的在結合不同的多媒體技術，以幫助聽語障人士與聽人間自然的溝通。
　　首先，第一個溝通輔助技術是發音學習和矯正系統。對於一套個人化的發音學習輔助系統而言，有效評估新使用者的發音錯誤模式是很重要的。在本方法中，我們透過估計個人化發音錯誤的機率達成這項評估。本文提出一個基於最小熵值計算的方法有效利用少量評估句子得到個人化發音錯誤機率。同時，由於對多數人而言，相同類型的發音錯誤通常都會同時出現。因此，當某些發音錯誤的機率變動時，相同類型發音錯誤的機率也會同時更新。
　　另一方面，以手語影片為基礎的手語影片合成系統則提供TaiSign 自然的手語輸出介面及聽語障人士與聽人間的手語溝通輔助功能。為了產生手語影片合成單元間的平滑內插影片片段，首先計算最小連接成本以決定兩個合成影片單元之最佳斷點。最小連接成本為距離成本、平滑成本、及影像失真成本的線性組合。兩斷點間的內插影片生成則利用非均勻有理B樣曲線(NURBS Curve)於手勢影像空間中建立一平滑曲線，並利用此曲線選擇最佳手部影像以達到手部影像內插之目的。當個別影像元件都準備完成後，我們使用影像疊合技術將所有影像元件合成為平滑之內插影片。
　　最後則是立體虛擬人物合成系統。此系統提供TaiSign 唇型動畫介面及發音輔助練習之視覺回饋。本系統首先拍攝真人唇型影帶，並由其中取得每個唇型合成單元的參數序列。為了達到唇型運動和語音輸出間的同步化，本系統使用最大方向改變演算法以根據語音速度決定唇型動畫參數變化。同時，為了達到在高語速下的最佳唇型運動平滑度，本系統結合貝茲曲線(Bernstein-Bézier curve)及近似運動理論提出四個音素相關的發音內插函式。

　　Based on recent advances in real-time database access and multimedia representation, computers have become instruments that can improve education quality, especially for the deaf. Numerous communication-aided approaches have been proposed and applied to enhance language and communication skills of the deaf. Since deaf people have difficulty in speaking, many communication-aided approaches, such as sign language, finger spelling, lip-reading, and total communication, have been proposed to enhance their language and communication skills. However, these systems have several drawbacks. For example, systems using only text or icon-based interfaces for sign language learning are ineffective. For learning sentence grammar, these systems provide only language fragments such as words, phrases, or fixed clauses. Consequently, users cannot readily understand the grammatical structure of a sentence translated from a sign language sequence. The approaches described in this dissertation including the method of communication skill learning, training, or teaching. A final system is developed for integrating all these approaches, called TaiSign. TaiSign is a novel communication skills learning system. The motivation in developing this system was to integrate different multimedia technologies, so as to assist deaf people in communicating naturally with hearing people.
　　The first technology is the pronunciation learning and rectification. For a personalized Computer Assisted Pronunciation Training (CAPT) system, efficient evaluation of mispronunciation patterns, which estimates the occurrence probabilities of personalized mispronunciation patterns, for a new user is important. This investigation presents an entropy-based approach to efficiently evaluating the personalized mispronunciation patterns using minimum number of evaluation sentences. Moreover, since people who mispronounce a phone usually mispronounce the other phones of similar pronunciation type, the probabilities of all mispronunciation patterns having high mutual information with the mispronounced phone of the same type are also updated. Besides, this investigation also provides a learning phase for users to rectify and practice the mispronounced phones.
　　The sign language synthesis system provides TaiSign an ability of sign language output and deaf-to-hearing communication assistance. We concentrated on a spatial interpolation approach using a Non-Uniform Rational B-spline (NURBS) function to produce a smooth interpolation curve. To generate movement epenthesis, the beginning and end “cut points” are determined based on the concatenation cost, which is a linear combination of the distance, smoothness and image distortion costs. An image component overlapping procedure is also employed to yield a smooth sign
video output.
　　Finally, a 3D virtual character is involved in TaiSign for the pronunciation learning. The virtual character is provided by a Text-To-Speech system. Motion parameters for each viseme are first constructed from video footage of a human speaker. To synchronize the parameter set sequence and speech signal, a maximum direction change algorithm is also proposed to select significant parameter set sequences according to the speech duration. Moreover, to improve the smoothness of co-articulation part under a high speaking rate, four phoneme-dependent co-articulation functions are generated by integrating the Bernstein-Bézier curve and apparent motion property.

TABLE OF CONTENT......................................................vii
LIST OF FIGURES.......................................................x
LIST OF TABLES........................................................xiii
CHAPTER 1. INTRODUCTION...............................................1
　1.1. Motivation.....................................................1
　1.2. The Approach of this Dissertation..............................2
　1.3. The Organization of this Dissertation..........................4
CHAPTER 2. SYSTEM OVERVIEW
TaiSign: A Communication Skills Learning System for the Deaf..........5
　2.1. Chapter Overview...............................................5
　2.2. TaiSign modules description....................................8
　　Sign Language Learning Module.....................................8
　　Sign Language-to-Text Translation Module..........................9
　　Text-to-Visual Speech Translation Module..........................11
　　Configuration Control Module......................................11
　2.3. Evaluation result..............................................12
　　Function Evaluation Setup.........................................12
　　Evaluation Result Analysis........................................14
　　Subject Analysis..................................................16
　　Performance Evaluation............................................17
　2.4. Summarization of this Chapter..................................18
CHAPTER 3. ENGLISH PRONUNCIATION RECTIFICATION
using Entropy-based Dynamic Sentence Selection........................19
　3.1. Chapter Overview...............................................19
　3.2. Personalized Mispronunciation Probability Estimation...........22
　　Evaluation Sentence Scoring and Selection.........................24
　　Probability Update for Mispronunciation Patterns of Similar Type..25
　3.3. Speech Input Verification......................................26
　　Acoustic model construction.......................................26
　　Speech Input Verification.........................................27
　3.4. Mispronunciation Type Definition...............................27
　　Typical Mispronunciation Type Statistics..........................27
　　Mispronunciation Pattern Detection................................29
　3.5. Experimental Results...........................................30
　　Segmentation Experiment on English Speech.........................30
　　Experiment on Speech Verification.................................30
　　Experiment on Mispronunciation Pattern Detection..................32
　　Experiment on Evaluation Sentence Selection.......................32
　3.6. Summarization of this Chapter..................................34
CHAPTER 4. VIDEO-BASED SIGN LANGUAGE SYNTHESIS
Movement Epenthesis Generation Using NURBS-Based Spatial
Interpolation.........................................................37
　4.1. Introduction...................................................37
　4.2. Description of NURBS Function..................................40
　4.3. Movement Epenthesis Generation.................................41
　　Sign-Language Database Construction...............................41
　　Problem Statement.................................................45
　　Concatenation Cost................................................46
　　Distance Cost.....................................................46
　　Trajectory Estimation and Smoothness Cost.........................47
　　Hand Image Selection and Image Distortion Cost....................49
　　Combination Coefficient Estimation................................50
　　Image Component Overlapping.......................................51
　4.4. Evaluation Results.............................................53
　　Estimation of Combination Coefficient in Concatenation Cost.......53
　　Comparison Between Original and Synthesized Sign-Language Videos..54
　　Evaluation of Trajectory..........................................56
　　Subjective Evaluation of Sign-Language Synthesis..................58
　4.5. Summarization of this Chapter..................................59
CHAPTER 5. TEXT-TO-VIRTUAL SPEECH
Generating Co-articulation under a Limited Frame Rate
Using Maximum Direction Change and Apparent Motion....................61
　5.1. Introduction...................................................61
　5.2. Motion Parameters Construction for Mandarin....................64
　　Unit Selection for Visual Speech Synthesis........................64
　　Feature Point Definition..........................................66
　5.3. Maximum Direction Change Algorithm.............................67
　5.4. Apparent Motion Analysis.......................................68
　5.5. Simulation Results.............................................73
　　Effect of the selection based on Maximum Direction Change.........73
　　Generation of transition component................................75
　5.6. The Integration of Tongue Animation............................76
　　Lip Animation for English viseme..................................77
　　3D Tongue animation...............................................77
　5.7. Summarization of this Chapter..................................78
CHAPTER 6. CONCLUSION AND FUTURE WORKS................................81
REFERENCE	.............................................................83
                                    

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校內：2007-11-14公開
校外：2007-11-14公開

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