近年來，隨著軟硬體技術發展迅速，智能裝置在我們的日常生活中提供各式各樣的便利服務，使用者只需透過簡單的命令即能與智能裝置進行互動。為了讓智能裝置能更人性化的為使用者提供服務，情感智能計算日漸成為重要議題。目前的智能裝置對於不同的使用者所進行的回應，多數屬於單調固定的回應，容易讓使用者感到無趣。我們希望讓智能裝置藉由簡短的互動，自動判斷出使用者類型，以針對不同的使用者人格特質，給予適合的回應內容。因此，改善智能裝置對使用者的人格特質識別就成為一項重要的研究議題。目前在人格特質相關研究中，學者的研究方向主要可以區分為聲音與文字兩方面。學者利用聲學特徵及文字特徵，去探討不同的特徵與人格之間的相關性。雖然在人格特質相關研究中已有相關顯著的成果，但是主要研究還是著重在單一個人的人格特質識別。在兩人互動過程中，感知彼此的人格特質相關研究主題則較少被學者提出。
本論文設計一套自動化人格特質感知的系統，從兩位語者的交談過程，分別對兩位語者進行動態的人格特質感知。本論文先利用回饋式神經網路架構 (Recurrent Neural Network, RNN) 對單回合人格特質觀測模型訓練語者單次應答文字特徵與五大人格特質面向(The Big-Five Personality Traits)的關係。再利用耦合隱藏式馬可夫模型 (Coupled Hidden Markov Model) 建立多回合人格特質觀測模型，訓練兩位語者在一次主題中的應答過程所呈現的人格特質。為了驗證本論文所提出的方法，我們建立一套自動化人格特質感知的系統，對於人格特質的整體正確率為71.9%。與傳統隱藏式馬可夫模型(HMM)及向量量化機(SVM)比較，本方法可獲得較佳之效能。由實驗可知論文所提之方法在實際應用上應能改善智能裝置單調的服務，針對不同的使用者，提供更豐富的回應。

In recent years, with the development of hardware and software technologies, intelligent devices offer a variety of convenient services in our daily life. Users can interact with those intelligent devices through a series of simple commands and feel that they are interacting with a real person. For intelligent devices can provide more personalized services for users, emotional intelligence computing is becoming an important issue. The responses in most of intelligent devices tend to be simple and monotonic, which makes users feel bored easily. Those intelligent devices could automatically distinguish between different users based on a brief interaction, then those intelligent devices can give a more appropriate response to the user according to the user’s personality traits. Therefore, how to identify the user's personality has become an important research topic. Recent research on personality trait detection are generally based on voice and text. Acoustic features and textual features are employed to explore the correlations between different personality traits. Although those studies have obtained significant achievements, few studies analyze mutual influence of two human personality traits in an interactive process.
In this thesis, an Automatic Personality Perception method is proposed. First, we establish the single speaker turn personality perception model by using the Recurrent Neural Networks to train the relationship between linguistic features and the big-five personality traits in each speaker's turn. Second, we establish the multiple-speaker turn personality perception model by using the Coupled Hidden Markov Model to observe two speaker’s personality across many speaker’s turns in each dialogue process. In order to evaluate the proposed method, an automatic personality perception system was constructed and the overall accuracy achieved 71.9%. Compared to traditional HMM-based and SVM-based methods, the proposed approach can obtain the highest performance. The promising results confirm the usability of this system for future applications.

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