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研究生:吳鑑峰
研究生(外文):Chien-Feng Wu
論文名稱:應用語音及臉部表情之雙模態情緒辨識
論文名稱(外文):Bimodal Emotion Recognition from Speech and Facial Expression
指導教授:吳宗憲吳宗憲引用關係
指導教授(外文):Chung-Hsien Wu
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:50
中文關鍵詞:鑑別性參數情緒辨識
外文關鍵詞:discriminative featuresemotion recognition
相關次數:
  • 被引用被引用:7
  • 點閱點閱:1843
  • 評分評分:
  • 下載下載:386
  • 收藏至我的研究室書目清單書目收藏:3
  隨著電腦科技日益精進,電腦已漸漸溶入人類的日常生活當中,因此具有智慧的人性化人機介面就成為重要的研究方向;讓電腦具有智慧其中一個重要的課題就是了解人類情緒;目前的研究,包括建立單一情緒辨識模組,及混合式雙模組架構,辨識模型主要採隱含式馬可夫模型、support vector machine或人工規則訂定等方法,而其中同步辨識效能及鑑別性參數擷取乃為相關研究的主要指標。
  在本論文中,主要探討影響情緒辨識之語音及臉部表情的特性,並建立具效能之同步辨識核心;其特定目標為:1).在參數分析部分,將參數進行鑑別性參數分析與選取,採用多變量分析中主成份分析的方法,經分析後,由解釋變異數百分比選擇主成份個數來決定參數集合的個數,並由矩陣F的積差相關係數決定每個參數集合所選取的參數;2).在訓練情緒辨識模組部分,以SVM及CDSVM的方法為基礎,並考量資料分布及分類正確性的改良方法-改良式CDSVM;3).以改良式CDSVM訓練情緒辨識模組,並辨識語音及臉部表情情緒。
  實驗部分,錄製360句情緒語音及其所對應之1440張臉部表情影像(3人),其中75%做為訓練,剩餘25%為測試;實驗結果顯示,鑑別性參數分析能有效找出鑑別性參數並提升模組辨識效能;以改良式CDSVM所訓練的情緒辨識模組辨識率,在語音情緒部分達66.55%,臉部表情部分達75.19%,而雙模態情況下為78.73%,與其他方法評比,在辨識率上,本論文方法有顯著的提升。
  With the trend of computer technology, computers have come into human’s daily life gradually. For this reason, human machine interface with intelligence and humanity become an important research issue. Human emotion recognition is one of the critical topics. Recent research on emotion recognition includes the construction of a single emotion recognizer using speech or facial expression, and mixture bimodal architecture. The recognition models include hidden Markov model, support vector machine and artificial rule-based, etc. Among these approaches, the main purpose is to extract discriminative features and therefore improve the recognition performance.
  In this paper, we investigate the features of speech and facial expression that affect the emotion recognition, and establish an effective recognition model. More specially, this study focuses on:1) analyzing and extracting discriminate features by using principal component analysis(PCA), 2) considering data distribution and classification accuracy into original SVM and CDSVM to build up a refined CDSVM, 3) finally, according to refined CDSVM, establishing a bimodal emotion recognizer to recognize human emotion.
  In order to evaluate our proposed approach, 360 emotion utterances and 1440 corresponding facial expressions were collected (3 persons). 75% for training, 25% for testing. Experimental results show that discriminative features analysis can find out discriminative features effectively and improve recognition performance. The emotion recognition accuracies of the emotion recognizer using refined CDSVM approach achieved 66.55%, 75.19% and 78.73% for speech, facial expression and bimodal, respectively. Our proposed method and architecture outperform other previous proposed approaches.
第一章 緒論 1
  1.1 前言 1
  1.2 研究動機與目的 1
  1.3 相關研究 2
  1.4 研究方法簡介 4
  1.5 章節概要 6
第二章 雙模態情緒辨識 7
  2.1 語音參數及臉部特徵擷取 7
  2.2 鑑別性參數分析與選取 18
  2.3 改良式CDSVM 25
  2.4 訓練情緒辨識模組 31
第三章 實驗評估 35
  3.1 實驗環境及設定 35
  3.2 鑑別性參數分析實驗 36
  3.3 Kernel function實驗 40
  3.4 合併模組比率實驗 42
  3.5 實驗比較 43
第四章 結論與未來展望 47
  4.1 結論 47
  4.2 未來展望 47 
參考文獻 48
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