人機介面在日常生活中扮演日益重要的角色，這促使了越來越多的研究人員投入人機互動研究領域，其中，對話系統是人機互動研究領域當中被廣泛探討的議題之一。雖然針對對話系統已進行了諸多研究，系統對於使用者往往只能作出單調的回應，然而，不同的使用者則是透過不同的社交風格去表達他們的意圖，為了達到更人性化互動的目的，系統應該採取靈活多變的對話回應。因此，系統如何去了解使用者在對話過程中展現的社交互動風格，對於實現和諧的人機互動是一個重要且創新的研究方向。
基於心理學家建議之最佳互動風格配對可以提供對話系統選擇適當的回應，本論文以探討互動風格辨識之研究議題為目標。根據互動風格之定義，透過人格特質與情緒狀態等心理因素來縮小低階特徵 (例如，韻律和語言特徵) 和高階互動風格之間的差距。所提出的交互關聯融合模型提供了一個機率架構來結合情緒、人格特質、初步互動風格結果，和互動風格歷史資訊之間的關係，以得到最後的互動風格辨識結果。實驗結果表明，交互關聯融合模型得到令人滿意地互動風格辨識結果，也證實了結合人格特質與情緒狀態等心理因素確實有效地提高互動風格辨識的準確性。
由於情緒感知在互動風格中扮演了重要的角色，在實現互動風格辨識的過程中，為了在真實對話的環境下獲得更好的語音情緒辨識結果，本論文進一步針對情緒展現在時間歷程上之演化模式做探討。除此之外，本論文更進一步地探討了在真實對話的情況下，說話因素造成人臉表情情緒辨識效能下降的問題。由於人們通常在說話的同時帶有情緒的展現，使得臉部表情的變化受到嘴巴周圍肌肉牽動產生影響，而為了解決這個問題，本論文提出一個特徵臉轉換為基礎的方法，利用平行資料庫(純表情和伴隨說話因素的表情)，透過統計式高斯混合模型建立內文相依的線性轉換函數，移除說話因素對於人臉表情造成的影響，以提升情緒辨識之準確性。實驗結果顯示，本論文所提出的方法優於目前文獻上的做法，證實移除說話因素有助於提升人臉表情情緒辨識的效能。

In today’s world, human-computer interface plays a key role in daily life. Researchers have been increasingly interested in the field of Human Computer Interaction (HCI). Dialogue systems, as one of the prominent HCI research areas, have been applied to a wide range of domains. Although significant efforts have been made in dialogue systems, an invariable or planned response is generally employed to respond to the speakers’ queries. However, speakers can express their intents in different social styles. Flexible and versatile responses should be taken into account to achieve more user-friendly interaction. Hence, determining how a speaker is engaged in a dialogue is crucial for achieving harmonious interaction between human and computer.
Based on the most appropriate Interaction Style (IS) pairs suggested by Berens for selecting a suitable response in a dialogue system, the interaction styles defined by Berens were chosen and adopted in this dissertation with the goal of tuning dialogue-system responses to the style of a computer user. According to the Berens model, personality trait and emotional state were considered in this dissertation as features to narrow the gap between low-level features (e.g., prosodic and linguistic features) and high-level interaction style. The proposed Fused Cross-Correlation Model (FCCM) provides a unified probabilistic framework to model the relationships among the psychological factors of emotion, personality trait, transient IS and IS history, for recognizing IS. The experimental results indicate that the proposed FCCM yields satisfactory results in IS recognition and also demonstrate that combining psychological factors effectively improves IS recognition accuracy.
Since emotion perception plays an important role in social interaction, to obtain a better speech emotion recognition result, we further focused on exploring the temporal evolution of an emotional expression. On the other hand, we also explore the problem of speaking effect for emotion recognition from facial expressions in real dialogue cases. When humans simultaneously express emotion while speaking, the changes of facial expression affected by muscle contractions of the mouth area may cause an unsatisfactory emotion recognition result. To manage this problem, an eigenface conversion-based approach is proposed; through a context-dependent linear conversion function modeled by a statistical Gaussian Mixture Model (GMM) is constructed with parallel data from speaking and non-speaking facial expressions with emotions, to remove speaking effect on facial expressions for improving accuracy of emotion recognition. Experimental results show that the proposed method outperforms current approaches and demonstrates that removing the speaking effect on facial expression is useful for improving the performance of emotion recognition.

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