在小組互動以及人機互動中，語者預測和話權預測是非常重要的任務。 我們日常生活中常見的流暢對話以及互動，都需要我們同時去整合三個問題，現在是誰在說話，下一個語者會是誰，下一個語者什麼時候會接話。人類在對話中分析到的細微行為差異對於機器學習而言是非常困難的。有很多學者使用了不同的行為特徵來當作預測語者的資訊，例如：視線的方向、講話語調或者是身體的姿勢手勢。在這篇研究中，我提出了一個可以同時考慮語者過去講話資訊以及考慮是否會發生講話狀態改變的模型。利用語者的講話傾向以及視線方向來預測下一個語者是誰，接著結合過去語者講話的資訊和是否會發生話語權的轉換來優化預測的表現。我的模型最後在 UAR 的表現達到 78.11%，贏過了原本 MultiMediate challenge 2021 冠軍模型 3.41%。

Next speaker prediction and turn change prediction are two important tasks in group interaction or human-agent interaction. In order to have a fluent and understandable conversation, we need to coordinate three questions, that is, who is currently speaking, who will be the next speaker and when the next speaker shall start to speak. Therefore, many researchers have investigated deeply in human’s subtle behaviors between these interactions. Behaviors such as gaze direction, speaking prosody or gestures have been utilized to become turn-taking cues for models to predict the next speaker. In this work, I proposed a decoder-based model dialogical speaker decoder (DSD) for next speaker prediction, which will coordinate speaker’s behavior features, such as talk tendency and gaze pattern, speaker’s past history of talking and speaking state transition detection model with timeawareness and behavior divergence. I have achieved next speaker prediction with UAR of 78.11%, which is 3.41% improvement over champion model in MultiMediate challenge 2021.

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