近年來，資訊科技的快速成長，不僅影響了人類的生活環境，更提升產業的工作效率，帶來更為多樣化且便利的生活品質，其中資訊科技的自動化服務，使顧客能夠快速地獲得服務，企業更能利用資訊科技的高運算能力，根據現有資源來提供顧客合適的服務項目，並協助企業制定出最有效的決策。目前企業在提供服務決策時，除了重視顧客的基本需求外，也越來越重視顧客心理的感受，而顧客情緒正會影響企業執行服務決策。情感運算越來越受重視，其概念為資訊科技能夠具備人類之情緒，並於人機互動的過程中，使資訊科技以人性的思維協助企業進行決策，本研究即是認為情緒除了傳達出對於事物的感受程度外，亦能夠表現出其對於服務的評價，因此資訊科技若能夠與人類一樣具有情緒能力，必能制定出更為人性化的決策。目前服務業在台灣產業結構中佔有非常大的比重，於此競爭激烈之市場環境中，企業為了提升其競爭能力，在服務提供上除了滿足顧客的基本需求外，亦期望能夠在服務互動過程中重視顧客之情緒變化，從顧客回饋中創造出服務的體驗價值。因此本研究認為，企業於進行服務決策時，若能夠將情感運算應用於服務決策中，即時地察覺顧客的情緒變化，並加以分析出最適合的補救項目，進而能夠快速且有效地解決顧客的問題。
本研究利用服務互動設計之概念，透過語音情緒辨識技術、類神經網路與決策樹分類法，建立一服務決策系統，並應用服務補救作為研究情境，驗證情緒在服誤決策提供上之成效。本研究之服務決策系統，運用情感運算之情緒辨識技術，偵測顧客於服務失誤發生時可能會產生的情緒反應，透過倒傳遞式類神經網路進行情緒之分類，並透過C4.5決策樹建立有效之服務補救規則，使系統能夠根據情緒反應與服務失誤情境來提供服務補救項目。為了建置出有效的服務決策系統，本研究利用Matlab R2013a來進行服務決策系統之設計，並設計了三個研究實驗透過四個餐飲服務業之服務失誤狀況，以語音情緒辨識之方式，分析顧客的情緒反應並提供出合適的服務補救項目，並根據顧客的期望補救項目排序來驗證服務決策的有效性。實驗一利用了倒傳遞式類神經網路來建立有效的情緒分類方法，並以試誤法找出適合本研究之類神經網路架構；實驗二透過網路問卷的方式，蒐集顧客對於服務失誤情境下的期望補救排序，其所收集之有效問卷共110筆，並透過C4.5決策樹之演算法，求出適合各情境下之關鍵屬性，用以建置出有效之服務補救決策規則；實驗三根據實驗一與實驗二之實驗結果，建置出服務決策系統，請35名受測者來對服務決策系統進行測試與評估，測試實驗一所建立之情緒分類準確度，以及實驗二所建立之服務決策規則準確度。
根據本研究實驗之結果，將情感運算結合服務互動設計之概念，所建置之服務決策系統能夠有效地進行情緒辨識，其情緒辨識準確率平均約為72.9%，且能夠提供符合顧客期望之服務補救項目，其服務決策準確率平均約為73.57%，而受測者對於此種服務決策方式，於各情境下所給予的服務體驗評價平均約為87.5%之高度滿意度。因此本研究發現，對企業而言，情緒不僅能夠作為服務決策提供之依據，更能將情緒應用在與顧客的服務互動當中，建立不同於以往的服務傳遞過程，創造顧客嶄新之服務體驗價值。企業在提供服務給顧客時，大致上仍是由顧客主動要求應提供何種服務項目，企業較鮮少去考量顧客獲得服務時的情緒狀況，因此根據本研究所提出之服務決策系統，企業能夠透過語音情緒辨識的方式，即時地了解顧客心理之需求，並提供合適的服務項目，提升顧客對企業之滿意度與忠誠度。本研究主要著重於語音情緒辨識之技術應用，並以服務補救之情境來驗證本服務決策系統之成效，未來期望能夠結合不同的情緒辨識技術，例如臉部表情、肢體動作偵測等等，分類出更多樣化的情緒類型，並透過普及運算之概念，將情緒靈活應用於更多的服務領域中，提供更多元化的服務項目。

With the development of information technology, information technology not only improves human’s living environments and the quality of life but also increases the productivity of industries. Self-service technology can efficiently to enable customers to acquire proper services. Meanwhile, information technology helps businesses to provide customers with quality services using existing resources and make the right and effective service decisions. Accordingly, businesses have to not only understand customer needs but also pay attention to customer emotions when they make service decisions. Affective computing is an important approach to recognize human’s emotions that information technology has high abilities of revealing and recognizing emotions to businesses to make effective service decisions. The service industry plays an important role in economic activities in Taiwan. Businesses need to create innovative valuable services to customers by understanding customer emotions within service encounters. Consequently, in order to dealing with customer problems, businesses should apply affective computing to service decision processes to recognizing customer emotions and deliver suitable services to customers.
This study aims to build a service decision system by adopting affective computing, artificial neural networks and decision trees based on the concept of service interaction design. This study uses service recovery as a case to test the service decision performance of the service decision system. Affective computing is to recognize customer emotions when customers encounter service failures. The customer emotions can be analyzed and clustered to positive and negative emotions via artificial neural networks. Then, this study tries to design effective service decision rules based on C4.5 algorithm of decision trees. This study builds the service decision system by using Matlab R2013a tool and defines four scenarios of service failures. Three experiments are conducted to evaluate the service decision system. Experiment 1 is to build an effective approach to classify customer emotions and to explore the proper analysis structure of artificial neural networks. Experiment 2 is to build suitable decision rules for the service decision system by using decision trees and surveying real data through 110 internet subjects. Experiment 3 is to evaluate the performance of the service decision system that 35 subjects are invited to experience the service decision system and respond to the service failures based on the experiment results of experiment 1 and experiment 2.
The experiment results show that the service decision system is built by combining the concept of service interaction design and affective computing which can have the high performance of customer recognition. The customer recognition rate is about 72.9%, the accuracy rate of service decision for customers is about 73.57 and the customer satisfaction rate for the service recovery is about 87.5%. Hence, according to the result findings, customer emotions can be a clue to enable businesses to make the right service decisions and create innovative service experiences for customers within service interactions. Meanwhile, in order to increase customer satisfaction and loyalty, businesses can effectively understand customer needs and requirements to deliver suitable services when customers encounter service failures. This study only focuses on recognizing customers’ speech to understand their emotions and use the service recovery as a case. Researchers continuously pay attention to the applications of combining face recognizing with action recognizing to cluster more emotion types. Besides, further research can elaborate the proper applications of service fields based on the idea of pervasive computing.

目錄
摘要	I
Abstract	III
 第一章	緒論	1
1.1	研究背景	1
1.2	研究動機	4
1.3	研究問題	6
1.4	研究目的	8
1.5	研究架構	9
 第二章	文獻探討	11
2.1	服務科學(Service science)	11
2.1.1	服務科學定義	12
2.1.2	服務導向邏輯(Service-dominant logic，S-D logic)	15
2.1.3	服務互動設計(Service interaction design)	17
2.1.4	小結	18
2.2	服務補救(Service recovery)	19
2.2.1	服務補救定義	20
2.2.2	服務失誤相關文獻	21
2.2.3	服務補救相關文獻	23
2.2.4	小結	27
2.3	情感運算(Affective computing)	27
2.3.1	情緒(Emotion)	28
2.3.2	情感運算定義	29
2.3.3	情感運算相關文獻	32
2.3.4	小結	35
2.4	類神經網路（Artificial neural networks，ANN）	36
2.4.1	類神經網路定義	37
2.4.2	類神經網路相關文獻	40
2.4.3	小結	44
2.5	結語	45
 第三章	研究方法	47
3.1	系統架構	47
3.2	語音訊號前處理(Acoustical pre-processing)	51
3.2.1	端點偵測(End-point detection，EPD)	53
3.2.2	預強調(Pre-emphasis)	54
3.2.3	音框化(Frame blocking)	55
3.2.4	漢明窗(Hamming window)	55
3.2.5	傅立葉轉換(Fourier transform)	56
3.2.6	梅爾三角帶通濾波器(Mel-scaled triangular filterbank)	57
3.2.7	餘弦能量轉換(Discrete cosine transform，DCT)	58
3.3	倒傳遞式類神經網路(Back propagation neural network)	59
3.3.1	倒傳遞式類神經網路之架構	59
3.3.2	倒傳遞式類神經網路之訓練	62
3.4	決策樹(Decision tree)	65
3.4.1	決策樹架構	65
3.4.2	決策樹演算法	66
3.4.3	決策樹C4.5之運算	68
3.4.4	小結	71
 第四章	實驗分析	72
4.1	實驗系統工具	72
4.2	實驗一：語音情緒分類能力之訓練與測試	73
4.2.1	實驗目的	73
4.2.2	實驗設計	74
4.2.3	實驗結果	80
4.3	實驗二：服務補救決策規則之建立	84
4.3.1	實驗目的	84
4.3.2	實驗設計	85
4.3.3	實驗結果	98
4.4	實驗三、服務決策系統之成效測試	103
4.4.1	實驗目的	103
4.4.2	實驗設計	104
4.4.3	實驗結果	111
4.4.4	實驗小結	116
4.5	討論	118
4.5.1	管理意涵	125
 第五章	結論	128
5.1	結論	128
5.2	研究貢獻	130
5.2.1	學術層面	130
5.2.2	實務層面	133
5.3	研究限制	135
5.4	未來研究方向	136
參考文獻	138
附錄	153

表目錄
表 2-1：國內生產毛額依行業分–分配比	11
表 2-2：服務科學之定義	14
表 2-3：服務失誤之分類	23
表 2-4：服務補救方式之分類	26
表 2-5：語音情緒辨識相關文獻之整理	34
表 2-6：類神經網路相關文獻之整理	42
表 4-1：類神經網路訓練與測試資料	77
表 4-2：倒傳遞式類神經網路之情緒識別結果(以測試樣本1-5為例)	80
表 4-3：類神經網路訓練結果(以隱藏層神經元個數4為例)	81
表 4-4：隱藏層神經元個數之類神經網路訓練成效結果	83
表 4-5：顧客期望補救項目之評分(以受測者001為例)	87
表 4-6：問卷資料整理結果(以受測者001-003為例)	87
表 4-7：實驗設計之問卷蒐集結果(基本資料)	88
表 4-8：問卷資料經數值處理後之結果(以受測者001-003，情境一為例)	89
表 4-9：各屬性之獲利比率(情境一)	92
表 4-10：各屬性之獲利比率(情境二)	93
表 4-11：各屬性之獲利比率(情境三)	93
表 4-12：各屬性之獲利比率(情境四)	93
表 4-13：k-fold交互驗證法之準確率測試(k=10、8、4，以情境一為例)	95
表 4-14：各情境下k-fold交互驗證法之準確率測試(k=10、8、4)	97
表 4-15：代號與問卷資料對照表	98
表 4-16：實驗資料筆數	112
表 4-17：語音情緒辨識結果(以受測者001為例)	113
表 4-18：語音情緒辨識準確率	113
表 4-19：服務失誤情境下之情緒識別率	113
表 4-20：受測問卷與系統服務補救結果之評估分數比較(以受測者001為例)	114
表 4-21：實驗三之服務補救項目評分與準確度	115
表 4-22：服務體驗評價百分比	116
表 4-23：本研究之語音情緒辨識成效與其他相關實驗系統比較表	117
表 4-24：情境三之決策結果，以〔性別=男性〕且〔婚姻=已婚有小孩〕為例	123
表 4-25：服務失誤情境之重視程度	124
表 4-26屬性〔平均每週出門用餐次數〕資料筆數比較表	125

圖目錄
圖 1-1：研究架構圖	10
圖 2-1：前饋式類神經網路(左：單層;右：多層)	39
圖 3-1：服務決策系統處理流程	47
圖 3-2：系統架構圖	49
圖 3-3：語音訊號前處理之流程圖(Ingale 2012)	52
圖 3-4：MFCC語音情緒特徵處理流程圖(Wang et.al 2008; Weng et.al 2010)	52
圖 3-5：倒傳遞式類神經網路架構圖	60
圖 3-6：雙彎曲函數	61
圖 3-7：倒傳遞式類神經網路之訓練流程圖	62
圖 3-8：決策樹架構	66
圖 4-1：實驗一之實驗流程	74
圖 4-2：正切雙彎曲轉換函數之示意圖	78
圖 4-3：對數雙彎曲轉換函數之示意圖	79
圖 4-4：類神經網路之訓練次數模擬收斂圖（左:500次；右:1000次）	82
圖 4-5：本實驗所建立之類神經網路架構圖	82
圖 4-6：類神經網路比較圖	83
圖 4-7：實驗二之實驗流程圖	85
圖 4-8：情境一之決策樹模型	98
圖 4-9：情境二之決策樹模型	99
圖 4-10：情境三之決策樹模型	101
圖 4-11：情境四之決策樹模型	102
圖 4-12：實驗三之實驗流程圖	104
圖 4-13：服務決策系統實作畫面	108

參考文獻
[1] 	行政院，＜民國102年行政院主計處–國民所得統計＞，網址：http://www.dgbas.gov.tw/public/data/dgbas03/bs4/nis93/ni.pdf
[2] 	Abdel-Hamid, O., Mohamed, A. R., Jiang, H., & Penn, G. (2012). Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition. In Acoustics, Speech and Signal Processing (ICASSP), 2012, March IEEE International Conference on (pp. 4277-4280). IEEE.
[3] 	Abe, T. (2005). What is service science. FRI Research Report no. 246, Fujitsu Research Institute,
[4] 	Alter, S. (2011). Making a science of service systems practical: Seeking usefulness and understandability while avoiding unnecessary assumptions and restrictions. In The Science of Service Systems (pp. 61-72). Springer US.
[5] 	Alter, S. (2012). Challenges for service science. Journal of Information Technology Theory and Application, 13(2), 22-38.
[6] 	Bachu, R., Kopparthi, S., Adapa, B., & Barkana, B. (2008). Separation of voiced and unvoiced using zero crossing rate and energy of the speech signal. American Society for Engineering Education (ASEE), Zone Conference Proceedings, 1-7.
[7] 	Baranyi, P., & Csapo, A. (2012). Definition and synergies of cognitive infocommunications. Acta Polytechnica Hungarica, 9(1), 67-83.
[8] 	Barile, S., & Polese, F. (2010). Smart service systems and viable service systems: Applying systems theory to service science. Service Science, 2(1-2), 21-40.
[9] 	Bengio, Y., & Grandvalet, Y. (2003). No unbiased estimator of the variance of K-fold cross-validation. CIRANO.
[10] 	Bitner, M. J., Booms, B. H., & Mohr, L. A. (1994). Critical service encounters: The employee's viewpoint. The Journal of Marketing, 58(4), 95-106.
[11] 	Bitner, M. J., Booms, B. H., & Tetreault, M. S. (1990). The service encounter: Diagnosing favorable and unfavorable incidents. Journal of Marketing, 54(1), 71-84.
[12] 	Bratko, I., & Muggleton, S. (1995). Applications of inductive logic programming. Communications of the ACM, 38(11), 65-70.
[13] 	Brooke, J. (1996). SUS-A quick and dirty usability scale. Usability Evaluation in Industry, 189, 194.
[14] 	Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W. F., & Weiss, B. (2005). A database of german emotional speech. Interspeech, Vol. 5, 1517-1520 
[15] 	Busso, C., Deng, Z., Yildirim, S., Bulut, M., Lee, C. M., Kazemzadeh, A Narayanan, S. (2004). Analysis of emotion recognition using facial expressions, speech and multimodal information. Proceedings of the 6th International Conference on Multimodal Interfaces, 205-211.
[16] 	Cambria, E., Livingstone, A., & Hussain, A. (2012). The hourglass of emotions. In Cognitive behavioural systems, Vol. 74(3), 144-157. Springer Berlin Heidelberg.
[17] 	Chang, P., Wang, Y., & Tsai, C. (2005). Evolving neural network for printed circuit board sales forecasting. Expert Systems with Applications, 29(1), 83-92.
[18] 	Chebat, J., & Slusarczyk, W. (2005). How emotions mediate the effects of perceived justice on loyalty in service recovery situations: An empirical study. Journal of Business Research, 58(5), 664-673.
[19] 	Chung, B. G., & Hoffman, K. D. (1998). Critical incidents: Service failures that matter most. The Cornell Hotel and Restaurant Administration Quarterly, 39(3), 66-71.
[20] 	Coiera, E. (2003). Interaction design theory. International Journal of Medical Informatics, 69(2), 205-222.
[21] 	Coleman, T., Branch, M. A., & Grace, A. (1999). Optimization toolbox. For use with MATLAB.User’s Guide for MATLAB 5, Version 2, Relaese II,
[22] 	Conlon, D. E., & Murray, N. M. (1996). Customer perceptions of corporate responses to product complaints: The role of explanations. Academy of Management Journal, 39(4), 1040-1056.
[23] 	Demirkan, H., Kauffman, R. J., Vayghan, J. A., Fill, H., Karagiannis, D., & Maglio, P. P. (2009). Service-oriented technology and management: Perspectives on research and practice for the coming decade. Electronic Commerce Research and Applications, 7(4), 356-376.
[24] 	DeWitt, T., Nguyen, D. T., & Marshall, R. (2008). Exploring customer loyalty following service recovery the mediating effects of trust and emotions. Journal of Service Research, 10(3), 269-281.
[25] 	Douglas-Cowie, E., Campbell, N., Cowie, R., & Roach, P. (2003). Emotional speech: Towards a new generation of databases. Speech Communication, 40(1), 33-60.
[26] 	Dube, L., & Menon, K. (2000). Multiple roles of consumption emotions in post-purchase satisfaction with extended service transactions. International Journal of Service Industry Management, 11(3), 287-304.
[27] 	Fahlman, S. E. (1988). An empirical study of learning speed in back-propagation networks.
[28] 	Ferrario, R., Guarino, N., Janiesch, C., Kiemes, T., Oberle, D., & Probst, F. (2011). Towards an ontological foundation of services science: The general service model.
[29] 	Fragopanagos, N., & Taylor, J. G. (2005). Emotion recognition in human–computer interaction. Neural Networks, 18(4), 389-405.
[30] 	Fredrickson, B. L. (2002). Positive emotions. Handbook of Positive Psychology, Vol. 3, 120-134.
[31] 	Gharavian, D., Sheikhan, M., Nazerieh, A., & Garoucy, S. (2012). Speech emotion recognition using FCBF feature selection method and GA-optimized fuzzy ARTMAP neural network. Neural Computing and Applications, 21(8), 2115-2126.
[32] 	Gronroos, C. (1988). Service quality: The six criteria of good perceived service quality. Review of Business, 9(3), 10-13
[33] 	Gronroos, C., & Gummerus, J. (2014). The service revolution and its marketing implications: service logic vs service-dominant logic. Managing Service Quality, 24(3), 206-229.
[34] 	Gronroos, C., & Gummerus, J. (2014). The service revolution and its marketing implications: Service logic versus service-dominant logic. Managing Service Quality, 24(3), 1-1.
[35] 	Guo, Z., Zhao, W., Lu, H., & Wang, J. (2012). Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renewable Energy, 37(1), 241-249.
[36] 	Hall, M. A., & Holmes, G. (2003). Benchmarking attribute selection techniques for discrete class data mining. Knowledge and Data Engineering, IEEE Transactions on, 15(6), 1437-1447.
[37] 	Han, J., Kamber, M., & Pei, J. (2006). Data mining: Concepts and techniques Morgan kaufmann.
[38] 	Han, W., Chan, C., Choy, C., & Pun, K. (2006). An efficient MFCC extraction method in speech recognition. Circuits and Systems, 2006. ISCAS 2006. Proceedings. 2006 IEEE International Symposium on, 4, 145-148.
[39] 	Hariharan, M., Saraswathy, J., Sindhu, R., Khairunizam, W., & Yaacob, S. (2012). Infant cry classification to identify asphyxia using time-frequency analysis and radial basis neural networks. Expert Systems with Applications, 39(10), 9515-9523.
[40] 	Hart, C. W., Heskett, J. L., & Sasser, W. E.,Jr. (1990). The profitable art of service recovery. Harvard Business Review, 68(4), 148-156.
[41] 	Hayes-Roth, B., Ball, G., Lisetti, C., Picard, R. W., & Stern, A. (1998). Panel on affect and emotion in the user interface. International Conference on Intelligent User Interfaces: Proceedings of the 3 Rd International Conference on Intelligent User Interfaces, 6(09), 91-94.
[42] 	Hecht-Nielsen, R. (1989). Theory of the backpropagation neural network. Neural Networks, 1989. IJCNN., International Joint Conference on, 593-605.
[43] 	Hess, R. L., Ganesan, S., & Klein, N. M. (2003). Service failure and recovery: The impact of relationship factors on customer satisfaction. Journal of the Academy of Marketing Science, 31(2), 127-145.
[44] 	Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.
[45] 	Hoffman, K. D., Kelley, S. W., & Rotalsky, H. M. (1995). Tracking service failures and employee recovery efforts. Journal of Services Marketing, 9(2), 49-61.
[46] 	Holmlid, S. (2009). Interaction design and service design: Expanding a comparison of design disciplines. Nordes, (2), 157-164.
[47] 	Holmlid, S., & Evenson, S. (2008). Bringing service design to service sciences, management and engineering. Service science, management and engineering education for the 21st century (pp. 341-345) Springer.
[48] 	Huang, H. C. (2012). Using artificial neural networks to predict restaurant industry service recovery. International Journal of Advancements in Computing Technology, 4(10), 315-321.
[49] 	Huang, K. Y. (2009). Challenges in human-computer interaction design for mobile devices. In Proceedings of the World Congress on Engineering and Computer Science, Vol. 1, 236-241.
[50] 	IBM Research (2004). Services science: a new academic discipline? A 120-page report of a two-day summit entitled Architecture of On-Demand Business, May 17-18, 2004.
[51] 	Inanoglu, Z., & Caneel, R. (2005). Emotive alert: HMM-based emotion detection in voicemail messages. Proceedings of the 10th International Conference on Intelligent User Interfaces, 251-253.
[52] 	Ingale, A. B., & Chaudhari, D. S. (2012). Speech emotion recognition. International Journal of Soft Computing and Engineering (IJSCE) ISSN, 2231-2307.
[53] 	Izgi, E., Oztopal, A., Yerli, B., Kaymak, M. K., & Şahin, A. D. (2012). Short–mid-term solar power prediction by using artificial neural networks. Solar Energy, 86(2), 725-733.
[54] 	Jiang, N., Zhao, Z., & Ren, L. (2003). Design of structural modular neural networks with genetic algorithm. Advances in Engineering Software, 34(1), 17-24.
[55] 	Junginger, S., & Sangiorgi, D. (2009). Service design and organizational change: Bridging the gap between rigour and relevance. 3rd IASDR Conference on Design Research, Seoul, Korea,
[56] 	Jyh-Shing Roger Jang, "Audio Signal Processing and Recognition," (in Chinese) available at the links for on-line courses at the author's homepage athttp://www.cs.nthu.edu.tw/~jang.
[57] 	Kaliouby, R. e., Picard, R., & BARON‐COHEN, S. (2006). Affective computing and autism. Annals of the New York Academy of Sciences, 1093(1), 228-248.
[58] 	Kelley, S. W., Hoffman, K. D., & Davis, M. A. (1994). A typology of retail failures and recoveries. Journal of retailing, 69(4), 429-452.
[59] 	Kinnunen, T. (2002). Designing a speaker-discriminative adaptive filter bank for speaker recognition. In INTERSPEECH.
[60] 	Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai, 14(2), 1137-1145.
[61] 	Kollias, S., & Karpouzis, K. (2005). Multimodal emotion recognition and expressivity analysis. Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on, 779-783.
[62] 	Komunda, M., & Osarenkhoe, A. (2012). Remedy or cure for service failure? Effects of service recovery on customer satisfaction and loyalty. Business Process Management Journal, 18(1), 82-103.
[63] 	Koolagudi, S. G., & Rao, K. S. (2012). Emotion recognition from speech using source, system, and prosodic features. International Journal of Speech Technology, 15(2), 265-289.
[64] 	Kort, B., Reilly, R., & Picard, R. W. (2001). An affective model of interplay between emotions and learning: Reengineering educational pedagogy-building a learning companion. Advanced Learning Technologies, 2001. Proceedings. IEEE International Conference on, 43-46.
[65] 	Krothapalli, S. R., Yadav, J., Sarkar, S., Koolagudi, S. G., & Vuppala, A. K. (2012). Neural network based feature transformation for emotion independent speaker identification. International Journal of Speech Technology, 15(3), 335-349.
[66] 	Lee, C. M., & Narayanan, S. S. (2005). Toward detecting emotions in spoken dialogs. Speech and Audio Processing, IEEE Transactions on, 13(2), 293-303.
[67] 	Lee, T. (2004). Back-propagation neural network for long-term tidal predictions. Ocean Engineering, 31(2), 225-238.
[68] 	Lek, S., & Guegan, J. (1999). Artificial neural networks as a tool in ecological modelling, an introduction. Ecological Modelling, 120(2), 65-73.
[69] 	Leon, E., Clarke, G., Callaghan, V., & Sepulveda, F. (2004). Real-time detection of emotional changes for inhabited environments. Computers & Graphics, 28(5), 635-642.
[70] 	Leon, E., Clarke, G., Callaghan, V., & Sepulveda, F. (2007). A user-independent real-time emotion recognition system for software agents in domestic environments. Engineering Applications of Artificial Intelligence, 20(3), 337-345.
[71] 	Lewis, B. R., & Spyrakopoulos, S. (2001). Service failures and recovery in retail banking: The customers’ perspective. International Journal of Bank Marketing, 19(1), 37-48.
[72] 	Li, Q., Zheng, J., Tsai, A., & Zhou, Q. (2002). Robust endpoint detection and energy normalization for real-time speech and speaker recognition. Speech and Audio Processing, IEEE Transactions on, 10(3), 146-157.
[73] 	Li, W., Zhang, Y., & Fu, Y. (2007). Speech emotion recognition in e-learning system based on affective computing. Natural Computation, 2007. ICNC 2007. Third International Conference on, Vol. 5, 809-813.
[74] 	Liao, W., Yang, D., & Hung, M. (2010). An intelligent recommendation model with a case study on u-tour taiwan of historical momuments and cultural heritage. Technologies and Applications of Artificial Intelligence (TAAI), 2010 International Conference on, 72-79.
[75] 	Lisetti, C., Nasoz, F., LeRouge, C., Ozyer, O., & Alvarez, K. (2003). Developing multimodal intelligent affective interfaces for tele-home health care. International Journal of Human-Computer Studies, 59(1), 245-255.
[76] 	Litman, D., & Forbes, K. (2003). Recognizing emotions from student speech in tutoring dialogues. Automatic Speech Recognition and Understanding, 2003. ASRU'03 2003. IEEE Workshop on, 25-30.
[77] 	Logan, B. (2000). Mel frequency cepstral coefficients for music modeling. ISMIR.
[78] 	Lusch, R. F. (2011). Reframing supply chain management: a service‐dominant logic perspective. Journal of Supply Chain Management, 47(1), 14-18.
[79] 	Lusch, R. F., & Vargo, S. L. (2006). Service-dominant logic: Reactions, reflections and refinements. Marketing Theory, 6(3), 281-288.
[80] 	Lusch, R. F., & Vargo, S. L. (2011). Service-dominant logic: A necessary step. European Journal of Marketing, 45(7/8), 1298-1309.
[81] 	Lusch, R. F., Vargo, S. L., & Tanniru, M. (2010). Service, value networks and learning. Journal of the Academy of Marketing Science, 38(1), 19-31.
[82] 	Madikeri, S. R., & Murthy, H. A. (2011). Mel filter bank energy-based slope feature and its application to speaker recognition. Communications (NCC), 2011 National Conference on, 1-4.
[83] 	Maglio, P. P., Srinivasan, S., Kreulen, J. T., & Spohrer, J. (2006). Service systems, service scientists, SSME, and innovation. Communications of the ACM, 49(7), 81-85.
[84] 	Mao, X., Zhang, B., & Luo, Y. (2007). Speech emotion recognition based on a hybrid of HMM/ANN. Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Informatics and Communications, 367-370.
[85] 	Mattila, A. S. (1999). An examination of factors affecting service recovery in a restaurant setting. Journal of Hospitality & Tourism Research, 23(3), 284-298.
[86] 	Miller, J. L., Craighead, C. W., & Karwan, K. R. (2000). Service recovery: A framework and empirical investigation. Journal of Operations Management, 18(4), 387-400.
[87] 	Murty, K. S. R., & Yegnanarayana, B. (2006). Combining evidence from residual phase and MFCC features for speaker recognition. Signal Processing Letters, IEEE, 13(1), 52-55.
[88] 	Ng, I., Maull, R., & Smith, L. (2011). Embedding the new discipline of service science. The science of service systems (pp. 13-35) Springer.
[89] 	Ng, I., Parry, G., Smith, L., Maull, R., & Briscoe, G. (2012). Transitioning from a goods-dominant to a service-dominant logic: Visualising the value proposition of Rolls-Royce. Journal of Service Management, 23(3), 416-439.
[90] 	Nicholson, J., Takahashi, K., & Nakatsu, R. (2000). Emotion recognition in speech using neural networks. Neural Computing & Applications, 9(4), 290-296.
[91] 	Nilsson, M., & Ejnarsson, M. (2002). Speech recognition using hidden markov model. Department of Telecommunications and Speech Processing, Blekinge Institute of Technology.
[92] 	Nwe, T. L., Foo, S. W., & De Silva, L. C. (2003). Speech emotion recognition using hidden markov models. Speech Communication, 41(4), 603-623.
[93] 	Pahlavan, R., Omid, M., & Akram, A. (2012). Energy input–output analysis and application of artificial neural networks for predicting greenhouse basil production. Energy, 37(1), 171-176.
[94] 	Petrushin, V. (1999). Emotion in speech: Recognition and application to call centers. Proceedings of Artificial Neural Networks in Engineering, pp. 7-10.
[95] 	Picard, R. W. (1999). Affective computing for HCI. Proceedings of HCI International (the 8th International Conference on Human-Computer Interaction) on Human-Computer Interaction: Ergonomics and User Interfaces, Vol. 1. 829-833.
[96] 	Picard, R. W. (2002). Affective medicine: Technology with emotional intelligence. Studies in Health Technology and Informatics, 80, 69-84.
[97] 	Picard, R. W. (2003). Affective computing: Challenges. International Journal of Human-Computer Studies, 59(1), 55-64.
[98] 	Picard, R. W. (1997) Affective computing. The MIT Press.
[99] 	Portnoff, M. (1976). Implementation of the digital phase vocoder using the fast fourier transform.Acoustics, Speech and Signal Processing, IEEE Transactions on, 24(3), 243-248.
[100] 	Quinlan, J. R. (1996). Improved use of continuous attributes in C4.5.
[101] 	Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1), 81-106.
[102] 	Quinlan, J. R. (1993). C4. 5: programs for machine learning, Vol. 1.
[103] 	Rabiner, L. R., & Schafer, R. W. (1979). Digital processing of speech signals IET.
[104] 	Ratanamahatana, C. a., & Gunopulos, D. (2003). Feature selection for the naive bayesian classifier using decision trees. Applied Artificial Intelligence, 17(5-6), 475-487.
[105] 	Reed, R. (1993). Pruning algorithms-a survey. Neural Networks, IEEE Transactions on, 4(5), 740-747.
[106] 	Ren, F., & Quan, C. (2012). Linguistic-based emotion analysis and recognition for measuring consumer satisfaction: an application of affective computing.Information Technology and Management, 13(4), 321-332.
[107] 	Rong, J., Li, G., & Chen, Y. P. (2009). Acoustic feature selection for automatic emotion recognition from speech. Information Processing & Management, 45(3), 315-328.
[108] 	Royse, C. F., Chung, F., Newman, S., Stygall, J., & Wilkinson, D. J. (2013). Predictors of patient satisfaction with anaesthesia and surgery care: a cohort study using the Postoperative Quality of Recovery Scale. European Journal of Anaesthesiology (EJA), 30(3), 106-110.
[109] 	Rummelhart, D. E. (1986). Learning representations by back-propagating errors.Nature, 323(9), 533-536.
[110] 	Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychological Review, 110(1), 145-172.
[111] 	Sabharwal, N., Soch, H., & Kaur, H. (2010). Are we satisfied with incompetent services? A scale development approach for service recovery. Journal of Services Research, 10(1), 125-142.
[112] 	Saco, R. M., & Goncalves, A. P. (2008). Service design: An appraisal. Design Management Review, 19(1), 10-19.
[113] 	Saffer, D. (2005). The Role of Metaphor in Interaction Design.
[114] 	Saffer, D. (2010). Designing for interaction: Creating smart applications and clever devices, New Riders Pub.
[115] 	Shneiderman, B. (2000). The limits of speech recognition. Communications of the ACM, 43(9), 63-65
[116] 	Smith, A. K., & Bolton, R. N. (2002). The effect of customers' emotional responses to service failures on their recovery effort evaluations and satisfaction judgments. Journal of the Academy of Marketing Science, 30(1), 5-23.
[117] 	Spohrer, J., & Kwan, S. K. (2008). Service science, management, engineering, and design (SSMED): Outline & references. The Future of Services: Trends and Perspectives, Vol. 1,107-232.
[118] 	Spohrer, J., Anderson, L., Pass, N., & Ager, T. (2008). Service science and service-dominant logic. Otago Forum, Vol. 2, 4-18.
[119] 	Spreng, R. A., Harrell, G. D., & Mackoy, R. D. (1995). Service recovery: Impact on satisfaction and intentions. Journal of Services Marketing, 9(1), 15-23.
[120] 	Tao, J., & Tan, T. (2005). Affective computing: A review. Affective computing and intelligent interaction, (pp. 981-995) Springer.
[121] 	Tax, S. S., & Brown, S. W. (2012). Recovering and learning from service failure. Sloan Management.
[122] 	Thomassey, S., & Fiordaliso, A. (2006). A hybrid sales forecasting system based on clustering and decision trees. Decision Support Systems, 42(1), 408-421.
[123] 	Vargo, S. L., Maglio, P. P., & Akaka, M. A. (2008). On value and value co-creation: A service systems and service logic perspective. European Management Journal, 26(3), 145-152.
[124] 	Vargo, Stephen L. and Robert F. Lusch (2006). Service-Dominant Logic: What it is, What it is not, What it might be. The Service-Dominant Logic of Marketing: Dialog, Debate, and Directions, 43-56.
[125] 	Vergin, R., & O'Shaughnessy, D. (1995). Pre-emphasis and speech recognition. Electrical and Computer Engineering, Vol. 2, 1062-1065.
[126] 	Vergin, R., O'Shaughnessy, D., & Gupta, V. (1996). Compensated mel frequency cepstrum coefficients. Acoustics, Speech, and Signal Processing, IEEE International Conference on, Vol. 1, 323-326.
[127] 	Vesterinen, E. (2001). Affective computing. Digital Media Research Seminar, Helsinki,
[128] 	Wang, C., Miao, Z., & Meng, X. (2008). Differential mfcc and vector quantization used for real-time speaker recognition system. Image and Signal Processing, Vol. 5, 319-323.
[129] 	Wan-I, L., Chia-Chi, L., Cheng-Wu, C., & Chieh-Shan, C. (2012). An experimental design of service failure, recovery and speed analysis in cloud service. African Journal of Business Management, 6(8), 3059-3064.
[130] 	Weiss, S., & Kulikowski, C. (1991). Computer systems that learn.
[131] 	Weng, Z., Li, L., & Guo, D. (2010). Speaker recognition using weighted dynamic MFCC based on GMM. Anti-Counterfeiting Security and Identification in Communication (ASID), 2010 International Conference on, 285-288.
[132] 	Wieland, H., Polese, F., Vargo, S. L., & Lusch, R. F. (2012). Toward a service (eco) systems perspective on value creation. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 3(3), 12-25.
[133] 	Wirtz, J., & Mattila, A. S. (2004). Consumer responses to compensation, speed of recovery and apology after a service failure. International Journal of Service Industry Management, 15(2), 150-166.
[134] 	Wirtz, J., & McColl-Kennedy, J. R. (2010). Opportunistic customer claiming during service recovery. Journal of the Academy of Marketing Science, 38(5), 654-675.
[135] 	Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., Philip, S. Y. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1-37.
[136] 	Yacoub, J., Takahashi, K., & Nakatsu, R. (2000). Emotion recognition in speech using neural networks. Neural Computing & Applications, 9(4), 290-296.
[137] 	Yacoub, S. M., Simske, S. J., Lin, X., & Burns, J. (2003). Recognition of emotions in interactive voice response systems. Interspeech.
[138] 	Yao, X. (1999). Evolving artificial neural networks. Proceedings of the IEEE, 87(9), 1423-1447.
[139] 	Yu, F., Chang, E., Xu, Y., & Shum, H. (2001). Emotion detection from speech to enrich multimedia content. Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing, Vol. 1, 550-557.
[140] 	Zeithaml, V. A., Parasuraman, A., & Berry, L. L. (1985). Problems and strategies in services marketing. The Journal of Marketing, 49(2), 33-46.
[141] 	Zemke, R., & Bell, C. (1990). Service recovery: Doing it right the second time. Training, 27(6), 42-48.
[142] 	Zheng, F., Zhang, G., & Song, Z. (2001). Comparison of different implementations of MFCC. Journal of Computer Science and Technology, 16(6), 582-589.
[143] 	Zhou, X., He, X., & Chen, B. (2002). Genetic algorithm based on new evaluation function and mutation model for training of BPNN. Tsinghua Science and Technology, 7(1), 28-31.