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研究生:張立穎
研究生(外文):Li-Ying Chang
論文名稱:以語音情緒辨識系統落實客服中心顧客經驗管理
論文名稱(外文):Customer Experience Management for Contact Center through Emotion Recognition System in Speech
指導教授:曹承礎曹承礎引用關係
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊管理學研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:48
中文關鍵詞:情緒辨識顧客經驗管理最近鄰居分類法
外文關鍵詞:Emotion RecognitionCustomer Experience Managementk Nearest Neighbor
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  • 收藏至我的研究室書目清單書目收藏:1
現今競爭激烈、以顧客為導向的市場,成功的企業不僅藉由商品與服務吸引顧客,
還必須創造良好的顧客體驗環境,使顧客在理智與情感的層面皆獲得滿足,藉此提高
顧客對於服務、對於企業的滿意度,最終達到增加顧客忠誠度與提高企業價值的目的。
顧客體驗是顧客在消費過程中,對於服務人員和銷售商品互動的情感體認,客服中心
不僅是企業提供服務的一環,也是顧客體驗環境中的議題。本研究針對客服中心顧客
經驗管理,藉由偵測顧客於客服電話中每次往來發言的情緒,分析顧客於客服電話過
程中的體驗。
目前語音情緒辨識相關研究,主要透過聲學特徵(Acoustic Information),結合
語言學方面之詞彙特徵,以及語料背景特徵辨識情緒,辨識上往往必須以人工操作許
多資料前處理,且由於透過扮演式語料訓練系統,其結果於自然對話之情緒辨認之效
能並不如預期。
本研究之客服中心語音情緒辨識系統,在辨識語音情緒自動化流程目標下,利用
自然式語音資料訓練系統,同時實行動態參數選取方法,針對不同訓練資料以不同參
數辨識。同時,考量不同發言者表達情緒不同,以個人量化特徵值作為參數進行辨識。
所得最佳正確率(Precision Rate)以及召回率(Recall Rate)分別為76%以及82%,和
現有系統與文獻相比,皆有不錯表現,同時驗證個人量化之語音特徵值,比之未量化
之語音特徵值,的確能增進語音情緒辨識效能。
表目錄 ........................................................................................................ 九
圖目錄 .................................................................................................... 一
第一章 緒論 ............................................................................................. 1
第一節 研究背景與動機 ..................................................................................... 1
第二節 研究目的 ................................................................................................. 4
第三節 論文架構 ................................................................................................. 5
第二章 文獻探討 ..................................................................................... 7
第一節 顧客經驗管理 ......................................................................................... 7
第二節 客服中心 ............................................................................................... 10
第三節 語音情緒辨識 ....................................................................................... 13
2.3.1 情緒分類與標示 ............................................................................ 13
2.3.2 語料來源 ........................................................................................ 15
2.3.3 辨識特徵 ........................................................................................ 17
2.3.4 分類器 ............................................................................................ 19
第三章 研究架構 ................................................................................... 21
第一節 系統架構 ............................................................................................... 21
第二節 設計目標 ............................................................................................... 22
第三節 解決方法 ............................................................................................... 24
第四章 系統實作 ................................................................................... 28
第一節 系統使用情境與流程 ........................................................................... 28
第二節 系統開發工具 ....................................................................................... 29
第五章 實驗設計與實驗結果分析 ....................................................... 31
第一節 實驗目的與設計 ................................................................................... 31
5.1.1 辨識正確率計算 ............................................................................ 32
5.1.2 K 折交叉驗證法 ............................................................................. 34
5.1.3 語音資料處理 ................................................................................ 35
第二節 實驗結果 ............................................................................................... 38
5.2.1 語論相似度計算 ............................................................................ 39
5.2.2 最近鄰居分類法k 值 .................................................................... 40

5.2.3 最佳特徵組合 ................................................................................ 41
5.2.4 個人化特徵值辨識效能 ................................................................ 42
第六章 結論與未來展望 ....................................................................... 44
第一節 結論 ....................................................................................................... 44
第二節 研究限制 ............................................................................................... 45
第三節 未來展望 ............................................................................................... 46
第七章 參考文獻 ................................................................................... 47
[1]C. Shaw and J. Ivens, Building great customer experiences: Palgrave Macmillan, Basingstoke, 2002.
[2]N. Millard, "Learning from the ‘wow’factor—how to engage customers through the design of effective affective customer experiences," BT Technology Journal, vol. 24, pp. 11-16, 2006.
[3]B. J. Pine 2nd and J. H. Gilmore, "Welcome to the experience economy," Harv Bus Rev, vol. 76, pp. 97-105, 1998.
[4]L. P. Carbone, "Clued In: How to Keep Customers Coming Back Again and Again," Financial Times Prentice Hall, 2004.
[5]B. Schmitt, Customer Experience Management: A Revolutionary Approach to Connecting with Your Customers: Wiley, 2003.
[6]G. Zaltman, How Customers Think: Essential Insights Into the Mind of the Market: Harvard Business School Press, 2003.
[7]J. Trigger and M. Harrison, "Six steps to excellent customer service," BT Technology Journal, vol. 24, pp. 117-126, 2006.
[8]A. R. Damasio, Descartes''error: emotion, reason, and the human brain: New York: GP Putnam, 1994.
[9]K. Dawson, The Call Center Handbook: The Complete Guide to Starting, Running, and Improving Your Call Center: Cmp Books, 2003.
[10]M. Sargent, "Customer service is the name of this game.," in Communication News, 2001, p. 54.
[11]J. Carlzon, Moments of truth: Ballinger Pub. Co Cambridge, Mass, 1987.
[12]E. Delorey, "Correlating IVR Performance and Customer Satisfaction," May, 2003.
[13]A. Jon, "Call center management by the numbers," Purdue Umversity Pre~ s, 1997.
[14]L. Devillers, L. Vidrascu, and L. Lamel, "Challenges in real-life emotion annotation and machine learning based detection," Neural Networks, vol. 18, pp. 407-422, 2005.
[15]P. Ekman, "An Argument for Basic Emotions," Emotion: Themes in the Philosophy of the Mind, 2005.
[16]C. M. Lee and S. S. Narayanan, "Toward detecting emotions in spoken dialogs," Speech and Audio Processing, IEEE Transactions on, vol. 13, pp. 293-303, 2005.
[17]I. Shafran, M. Riley, and M. Mohri, "Voice signatures," Automatic Speech Recognition and Understanding, 2003. ASRU''03. 2003 IEEE Workshop on, pp. 31-36.
[18]C. M. Lee, S. S. Narayanan, and R. Pieraccini, "Combining Acoustic and Language Information for Emotion Recognition," 2002.
[19]D. J. Litman and K. Forbes-Riley, "Recognizing student emotions and attitudes on the basis of utterances in spoken tutoring dialogues with both human and computer tutors," Speech Communication, vol. 48, pp. 559-590, 2006.
[20]A. Batliner, K. Fischer, R. Huber, J. Spilker, and E. Noth, "Desperately Seeking Emotions or: Actors, Wizards, and Human Beings," 2000.
[21]R. Tato, R. Santos, R. Kompe, and J. M. Pardo, "Emotional Space Improves Emotion Recognition," 2002.
[22]R. Cowie and R. R. Cornelius, "Describing the emotional states that are expressed in speech," Speech Communication, vol. 40, pp. 5-32, 2003.
[23]L. Devillers, L. Lamel, I. Vasilescu, C. Limsi, and F. Orsay, "Emotion detection in task-oriented spoken dialogues," Multimedia and Expo, 2003. ICME''03. Proceedings. 2003 International Conference on, vol. 3, 2003.
[24]C. Pereira, "Dimensions of emotional meaning in speech," 2000.
[25]R. Plutchik, The psychology and biology of emotion: HarperCollins College Publishers, New York, NY, 1994.
[26]M. Shami and W. Verhelst, "An evaluation of the robustness of existing supervised machine learning approaches to the classification of emotions in speech," Speech Communication, vol. 49, pp. 201-212, 2007.
[27]P. Boersma, "Praat, a system for doing phonetics by computer," Glot International, vol. 5, pp. 341-345, 2001.
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