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研究生:鍾舜昌
研究生(外文):Zhong, Shun-Chang
論文名稱:圖像對話聲學神經網絡: 在預測任務績效時對人格與聲學行為之間的小組內和小組間影響建模
論文名稱(外文):A Graph Interlocutor Acoustic Network: Modeling Intra-group and Inter-group Effects between Personality and Vocal Behavior in Predicting Task Performances
指導教授:李祈均李祈均引用關係
指導教授(外文):Lee, Chi-Chun
口試委員:李秀珠冀泰石胡敏君
口試委員(外文):Li, Shu-ChuChi, Tai-ShihHu, Min-Chun
口試日期:2020-08-17
學位類別:碩士
校院名稱:國立清華大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:109
語文別:英文
論文頁數:52
中文關鍵詞:小組互動個性注意力機制圖卷積網絡
外文關鍵詞:group interactionpersonalityattention mechanismgraph convolutional networks
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  • 下載下載:27
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最近的研究表明,小組成員之間的個性複合效應與整體小組績效有關,整合小組級個性和聲音行為可以增強在小組互動過程預測任務績效的預測能力。在預測小組任務績效時,我們建議應該從組內和組間兩個角度來建模小組成員的個性對於聲音行為的影響。具體來說,我們提出了一種基於圖學的複合型神經網絡:圖像對話聲學神經網絡(G-IAN)架構,用於建模小組級別的個性組成與成員的聲學行為之間的組內效應;此外,它利用組間的個性組成相似性生成圖表,以學習預測任務績效的組間關係。我們的結果顯示,G-IAN在NTULP和GGID數據庫上均實現了良好的預測效能,分類準確率在想個資料庫上分別達到了72.2%和78.4%,這比僅對聲音行為進行建模的基線模型絕對要高14%。我們也在ELEA資料庫上驗證了G-IAN的效能,並達到了64.97 MSE(0.409皮爾遜相關度)的預測效能,優於目前相關研究的預測效能。此外,我們在二維的人格空間進行可視化分析,以進一步闡明群體之間的結構關係。
Recent studies have indicated that the composite personality effect between group members is related to the overall group performance, the integration of group-level personality and vocal behaviors can provide enhanced prediction power on task performance for small group interactions. In this work, we propose that the impact of member personality for task performance prediction in groups should be explicitly modeled form both intra and inter-group perspectives. Specifically, we propose the Graph Interlocutor Acoustic Network (G-IAN) architecture that models the intra-group effect between group-level personality composition and members’ vocal behavior; additionally, it utilizes the similarity of personality composition between groups as the graph to learns the inter-group relationship in predicting the task performance. Our result shows that G-IAN achieves the promising performance, 72.2% and 78.4% task performance classification accuracy in both NTULP and GGID datasets, which outperforms the baseline model that models vocal behavior only by 14%; it also presents the state-of-art performance at 64.97 MSE better than the current best methods in ELEA dataset. Moreover, we present visualization analysis on the 2D personality space to further illuminate the structural relationship between groups.
誌謝
摘要
Abstract
Contents
Chapter 1 Introduction--------------------1
Chapter 2 Database and Features-----------6
Chapter 3 Research Methodology------------14
Chapter 4 Experiments and Results---------26
Chapter 5 Conclusion----------------------47
Reference---------------------------------49
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