運用語音數據判別客戶滿意度

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姓名

柯彥輝(YEN-HUEI KO) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

運用語音數據判別客戶滿意度
(Distinguishing Customer Satisfaction with Vocal Data)

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摘要(中)

根據客戶消費行為研究，客戶滿意度明顯影響客戶回購意願，產品銷售業績和企業成長。通常，滿意度測量需要客戶花費額外的時間來填寫購買後的問卷調查。然而，在電話行銷產業，問卷調查仍存在一些執行上的限制，因此，開發一種更方便收集數據的方法及無需客戶購買後填寫問卷調查即可直接評估客戶滿意度的工具，是非常值得研究。
這項研究試圖驗證，透過語音數據分析是否可以用來區分客戶滿意度。本研究完成了以下任務：設計一個實驗流程來收集客戶表達滿意度的聲音和可驗證相應此聲音的真實數據；根據收集到的數據，將參與者分為滿意、中立和不滿意；從客戶表達滿意度的聲音數據中提取MFCCs（梅爾頻率倒譜係數）作為特徵；由於所收集到的聲音數據集有限，使用Auto-Encoder (自動編碼器) 進一步減少聲音的向量維度；所提取的MFCCs和音韻特徵被輸入到LSTM-RNNs（長期短期記憶-遞歸循環神經網絡）和SVM (支持向量機)，以建立區分客戶滿意度的模型；使用nested cross-validation (雙迴圈交叉驗證) 訓練和評估模型；SVM 和LSTM 的平均準確率分別可以達到 73.97% 和 71.95%。

摘要(英)

Customer repurchase behavior is manifestly influenced by satisfaction and the degree of customer satisfaction impacts on business growth and enterprise performance. Typically, satisfaction measurement requires customers to spend additional time to fill in a post-purchase survey. A more convenient way to collect data is to ask customers to express their degree of satisfaction while they are actually using the product or service they have purchased. This study strived to verify if voices can be used to distinguish customer satisfaction. An experiment was set up to collect voices and survey of satisfaction right after participants consume beverage offered. Participants were clustered into satisfaction, neutrality, and dissatisfaction according to the collected questionnaires. The MFCCs (Mel Frequency Cepstral Coefficients) were extracted from voices as features. Due to the dataset was limited, Auto-Encoder was used to reduce the voice features. The extracted and prosodic features were fed into an LSTM-RNNs (Long Short-Term Memory-Recurrent Neural Networks) and SVM models to distinguish customer satisfaction. The nested cross-validation method was used to train and evaluate models. The average accuracy of SVM and LSTM could achieve 73.97% and 71.95% of accuracy, respectively.

關鍵字(中)

★ 顧客滿意度
★ 梅爾頻率倒譜系數
★ 長短期記憶模型-遞歸循環神經網路
★ 支援向量機

關鍵字(英)

★ Customer satisfaction
★ MFCCs
★ LSTM
★ SVM

論文目次

中文摘要 i
Abstract ii
Preface iii
Table of Contents iv
List of Figures v
List of Tables vi
Chapter Ⅰ Introduction 1
Chapter Ⅱ Related works 5
2-1 Expectancy confirmation theory 5
2-2 Applications of MFCCs 6
Chapter Ⅲ Methodology 9
3-1 Research design 9
3-2 Construction of satisfaction dataset 11
3-3 MFCC processing 14
3-4 Auto-Encoder neural networks 21
3-5 LSTM model construction 23
3-6 SVM model construction 27
Chapter Ⅳ Experimental results and analysis 29
4-1 Experimental data collection and analysis 29
4-1-1 Statistical analysis and validation 29
4-1-2 Acoustic extraction 33
4-1-3 Auto-Encoder for feature reconstruction 34
4-2 Experimental results 37
4-2-1 Nested cross validation 37
4-2-2 Classification 40
Chapter Ⅴ Conclusion 49
5-1 Implications 50
5-2 Limitation and further research 51
Acknowledgment 52
Reference 53

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指導教授

許秉瑜(Ping-Yu Hsu)

審核日期

2023-1-13

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