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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃從仁(Tsung-Ren Huang) | |
| dc.contributor.author | Shin-Min Hsu | en |
| dc.contributor.author | 徐歆閔 | zh_TW |
| dc.date.accessioned | 2023-03-19T23:21:24Z | - |
| dc.date.copyright | 2022-07-05 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2022-06-23 | |
| dc.identifier.citation | Chinese References 黃金蘭, 林以正, 謝亦泰, 程威銓, Huang), 黃金蘭(Chin-Lan, Chung, C. K., Hui, N., Lin), 林以正(Yi-Cheng, Seih), 謝亦泰(Yi-Tai, Lam, B. C. P., Chen), 程威銓(Wei-Chuan, Bond, M. H., & Pennebaker, J. W. (2012). 中文版「語文探索與字詞計算」詞典之建立. 中華心理學刊, 54(2), 185–201. https://doi.org/10.6129/CJP.2012.5402.04 English References Abadi, M. K., Correa, J. A. M., Wache, J., Heng Yang, Patras, I., & Sebe, N. (2015). Inference of personality traits and affect schedule by analysis of spontaneous reactions to affective videos. 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 1–8. https://doi.org/10.1109/FG.2015.7163100 Agaibi, C. E., & Wilson, J. P. (2005). Trauma, PTSD, and resilience: A review of the literature. Trauma, Violence, & Abuse, 6(3), 195–216. https://doi.org/10.1177/1524838005277438 Akinnaso, F. N. (1982). On the differences between spoken and written language. 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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/85682 | - |
| dc.description.abstract | 大五人格和心理韌性是個人特質中相當重要的面向，可以被廣泛運用於學術界和業界，例如人事選拔或臨床心理領域。至今為止，其測量方式多半仰賴自陳式量表，不僅耗時耗力，也容易受填答者的主觀意識而影響作答。因此，本研究旨在藉由人機互動中所記錄的言語訊號與心電圖的振幅強度（Electrocardiogram (ECG) amplitude）、心率變異分析（Heart Rate Variability, HRV）和膚電生理回饋（Galvanic Skin Response, GSR）等生理訊號來預測大五人格特質和心理韌性。我們搜集32位受試者所提取並陳述的六段記憶——涵蓋正負向和三個時期（國小、國高中、大學）——以探討正負向情境和時間的潛在干擾並同時紀錄其對應的生理與語音訊號。首先，我們進行各訊號和問卷中各向度的相關分析來萃取有用的特徵訊號，再利用機器學習技術於這些特徵訊號來建立能預測個人特徵高低的二元分類模型。結果顯示：聲音訊號和生理訊號都能夠有效地預測大五人格和心理韌性的程度；預測模型對於高分組與低分組的二元區分可達0.68~0.86的F1分數。此研究證明行為與生理資料能夠有效地預測性格與韌性等重要個人特質。 | zh_TW |
| dc.description.abstract | Big-five personality and resilience are important personality characteristics and targets of interest in academic and industrial domains, such as personnel selection and clinical psychology. So far, the measurements of these two personal characteristics mainly rely on self-reported questionnaires, which are tedious and prone to response biases. To address these issues, the present study explored the possibility of predicting one’s resilience and big-five personality traits using speech and physiological signals measured during human-robot interactions. The audio features were extracted using OpenSMILE; the word usage was categorized using Linguistic Inquiry and Word Count (LIWC). The physiological signals include Electrocardiogram (ECG) amplitude, Heart Rate Variability (HRV), and Galvanic Skin Response (GSR). We asked 32 participants to retrieve six memories—positive and negative memories across three different periods—to balance the influence of valence. In the meantime, we recorded their audio and physiological signals during memory interpretation. We first examine correlations between personality traits and behavioral data and then build binary prediction models for factors in the big-five model and resilience. Results suggest that the audio modality and the physiological signals can serve as effective methods for predicting personality and resilience. Best achieved F1-scores range from 0.68 to 0.86 depending on different traits. Our research confirmed that behavioral features could provide effective cues for recognizing personal traits. To our knowledge, this is the first research using both speech and physiological signals to predict resilience. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T23:21:24Z (GMT). No. of bitstreams: 1 U0001-1706202210441800.pdf: 2162443 bytes, checksum: 3fc88e04aed9a740f2fe130775278a3a (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 口試委員會審定書…………………………………………………………………… i 誌謝………………………………………………………………………………… ii 中文摘要………………………………………………………………………… iii 英文摘要………………………………………………………………………… iv Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 2 1.3 Thesis Organization 3 Chapter 2 Theoretical Concepts 4 2.1 Personality Traits 4 2.2 Resilience 6 2.3 Physiological Signals 8 2.4 Speech Signals 11 Chapter 3 Personality Computing 13 3.1 Personality and Audio Features 14 3.2 Personality and Linguistic Features 16 3.3 Personality and Physiological Signals 18 Chapter 4 Methods 21 4.1 Questionnaires 21 4.2 Physiological Signals Recording and Features Extraction 22 4.3 Speech Signals Recording and Features Extraction 26 4.4 Features Selection 30 4.5 Classification 31 Chapter 5 Experiment 33 5.1 Experiment Description 33 5.2 Procedure 33 5.3 Participants 35 Chapter 6 Results 36 6.1 Questionnaires 36 6.2 Correlational Analysis 40 6.3 Classification Results 51 6.4 Best Classifiers 54 Chapter 7 Discussion 56 7.1 Correlational Analysis 56 7.2 Classification Results 59 7.3 Contributions and Limitations 60 Chapter 8 Conclusions 62 References 63 Chinese References 63 English References 63 Appendix 77 | |
| dc.language.iso | zh-TW | |
| dc.subject | 人機互動 | zh_TW |
| dc.subject | 心電圖 | zh_TW |
| dc.subject | 心率變異分析 | zh_TW |
| dc.subject | 膚電生理回饋 | zh_TW |
| dc.subject | 語文探索與字詞計算 | zh_TW |
| dc.subject | 語音訊號 | zh_TW |
| dc.subject | 自動辨識人格特質 | zh_TW |
| dc.subject | 心理韌性 | zh_TW |
| dc.subject | 大五人格特質 | zh_TW |
| dc.subject | ECG | en |
| dc.subject | human-robot interaction | en |
| dc.subject | big-five personality traits | en |
| dc.subject | resilience | en |
| dc.subject | automatic personality recognition | en |
| dc.subject | audio features | en |
| dc.subject | LIWC | en |
| dc.subject | GSR | en |
| dc.subject | HRV | en |
| dc.title | 人格特質和心理韌性可利用生理和言語訊號預測 | zh_TW |
| dc.title | Personality and Resilience Are Predictable by Physiological and Speech Signals | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 曹昱(Yu Tsao),陳淑惠(Sue-Huei Chen),張仁和(Jen-Ho Chang) | |
| dc.subject.keyword | 心電圖,心率變異分析,膚電生理回饋,語文探索與字詞計算,語音訊號,自動辨識人格特質,心理韌性,大五人格特質,人機互動, | zh_TW |
| dc.subject.keyword | ECG,HRV,GSR,LIWC,audio features,automatic personality recognition,resilience,big-five personality traits,human-robot interaction, | en |
| dc.relation.page | 108 | |
| dc.identifier.doi | 10.6342/NTU202200981 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2022-06-23 | |
| dc.contributor.author-college | 理學院 | zh_TW |
| dc.contributor.author-dept | 心理學研究所 | zh_TW |
| dc.date.embargo-lift | 2022-07-05 | - |
| 顯示於系所單位： | 心理學系 | |
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