利用LSTM建立聲音滿意度辨識模型

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姓名

沈依(Yi Shen) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

利用LSTM建立聲音滿意度辨識模型
(Construct a vocal satisfaction identification model with LSTM)

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至系統瀏覽論文 (2025-7-15以後開放)

摘要(中)

隨著行動電話的蓬勃發展，電話已儼然成為行銷的強大工具，並且以電戶行銷的方式可客製化的制定適合的策略以達到顧客滿意，藉此特性每日皆會有大量地聲音資料產生。然而，對於電話行銷而言，若要探討顧客滿意度，無法使用傳統紙本問卷之形式蒐集，必須透過語音對話的方式進行探討，而目前已有學者透過語音資料以情感或是情緒探討滿意度，但無論是情緒或情感皆是多元且複雜的，其正負向之情感或情緒皆不能代表顧客的滿意程度，因為正向可能包含了開心、快樂、滿意，負向可能包含沮喪、難過、不滿意，而滿意以及不滿意皆只是其中可能的結果，且目前尚未有學者利用真實的客服中心語音資料建立滿意度辨識模型。故本研究將建立一套LSTM聲音滿意度辨識模型，結合真實客服滿意度調查語音資料與顧客購買之歷史紀錄，以探究顧客真實的滿意度感受。而本研究首先以實驗設計蒐集實驗室資料以建立實驗室滿意度辨識模型，並以此模型當作業界模型之雛形，再以相同的方式建立業界滿意度辨識模型。然而，最後實驗結果得出實驗室與業界滿意度辨識模型精確率皆高於70%，依此結果，此模型可有效地了解顧客之滿意程度，並可掌控電話行銷進行之時間，以降低成本及提升客服人員服務品質管理以及顧客關係管理的維護，使企業增加效能與利益。

摘要(英)

As the rapid development of technology, smart phone becomes a powerful tool for marketing and telephone marketing can customize the suitable strategies to make customer feel satisfied, thus it would generate a large amount of vocal data every day. However, if we want to know the satisfaction of telephone marketing, we must use the vocal data instead of paper questionnaire. There are some scholars using vocal data to discuss satisfaction by emotion or sentiment and divide it in to positive and negative, but whether the emotion or sentiment are diverse and complex, the positive may include happy, excited and satisfied and the negative may include frustrated, anger and dissatisfied, according to the above, satisfied and dissatisfied are just the possible results, so we can’t use it to represent the satisfaction of the customer, and no scholars have used the real call center’s vocal data to construct a satisfaction identification model. Therefore, this study will construct a vocal satisfaction identification model with LSTM, it combines with real vocal data of satisfaction survey and the history records of customer purchase, to explore the real customer satisfaction. In this study, the identification model derived from the vocal collected in the lab serves as a prototype to develop the satisfaction identification model for vocal collected from call centers. Based on the result, the accuracy of these two models are higher than 70%. To sum up, this model can effectively identify customer satisfaction and control the duration of a call, it can reduce costs, improve the service quality, maintain customer relationship, enhance company’s efficiency and benefits.

關鍵字(中)

★ 電話行銷
★ 顧客滿意度
★ LSTM

關鍵字(英)

★ Telephone marketing
★ Customer Satisfaction
★ LSTM

論文目次

目錄
中文摘要 I
ABSTRACT II
目錄 III
圖目錄 V
表目錄 VI
第一章緒論 1
1-1 研究背景與動機 1
1-2 研究目的 4
1-3 研究架構 5
第二章文獻探討 6
2-1 顧客滿意度 6
2-2 顧客滿意度與再購意願 8
2-3 語音情緒辨識 9
第三章研究方法 12
3-1 研究流程 12
3-2 聲音處理與特徵萃取 13
3-3 遞迴神經網路 17
第四章研究實驗 20
4-1 資料來源 20
4-2 資料蒐集流程 21
4-2-1 實驗室資料蒐集流程 21
4-2-2 業界資料蒐集流程 25
4-3 實驗結果與分析 29
4-3-1 實驗室資料信效度分析 29
4-3-2 實驗室與業界資料結果比較與分析 31
第五章結論 37
5-1 研究結論 37
5-2研究限制與未來研究建議 38
參考文獻 39
附錄A 實務建議 44
顧客一年未購買及不滿意原因分析 44
對業界之建議 45

圖目錄
圖1-1 : 研究架構圖 5
圖3-1：研究流程圖 12
圖3-2：梅爾倒頻譜係數萃取流程 13
圖3-3：RNN架構 17
圖3-4：LSTM架構 18
圖4-1：前測問卷 22
圖4-2：朗讀字卡 22
圖4-3：後測問卷 22
圖4-4：實驗室資料蒐集流程 24
圖4-5：滿意顧客詢問流程 26
圖4-6：不滿意顧客詢問流程 26
圖4-7：業界資料蒐集流程 28
圖4-8：實驗室資料在不同學習率與神經元數量下之精確率 33
圖4-9：業界資料在不同學習率與神經元數量下之精確率 34
圖4-10：13與26和39特徵維度之精確率差異 35
圖A-1：第一等第之顧客電話行銷時間軸 47
圖A-2：第二等第之顧客電話行銷時間軸 47
圖A-3：第三等第之顧客電話行銷時間軸 48

表目錄
表2-1：顧客滿意度之定義 6
表2-2：語音情緒辨識文獻比較 10
表4-1：實驗室與業界資料來源 20
表4-2：顧客分類推論 26
表4-3：滿意問卷混淆矩陣表 31
表4-4：梅爾倒頻譜架構特徵萃取之參數 31
表4-5：實驗室混淆矩陣表 33
表4-6：實驗室模型LSTM參數表 33
表4-7：業界混淆矩陣表 34
表4-8：業界模型LSTM參數表 34
表4-9：實驗室模型測試業界資料之混淆矩陣表 35
表A-1：顧客等第狀態代號表 49

參考文獻

參考文獻
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指導教授

許秉瑜(Ping-Yu Hsu)

審核日期

2020-7-15

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