Game-Time：評估語音語言模型中的時間動態特性

摘要

對話式口語語言模型（SLMs）正逐漸成為實時語音互動的一個有前景的範式。然而，其時間動態能力，包括管理時機、節奏和同時說話的能力，仍然是對話流暢性的一個關鍵且未經評估的挑戰。為解決這一問題，我們引入了「遊戲時間基準」（Game-Time Benchmark），這是一個系統性評估這些時間能力的框架。受人類通過語言活動學習語言的啟發，遊戲時間基準包括基本的指令跟隨任務和具有時間約束的高級任務，例如節奏遵守和同步回應。我們對多種SLM架構的評估揭示了明顯的性能差距：雖然最先進的模型在基本任務上表現良好，但許多當代系統仍然在基本的指令跟隨上遇到困難。更為關鍵的是，幾乎所有模型在時間約束下都顯著退化，暴露了在時間意識和全雙工互動上的持續弱點。遊戲時間基準為引導未來研究朝向更具時間意識的對話式AI提供了基礎。演示和數據集可在我們的項目網站https://ga642381.github.io/Game-Time上獲取。

English

Conversational Spoken Language Models (SLMs) are emerging as a promising paradigm for real-time speech interaction. However, their capacity of temporal dynamics, including the ability to manage timing, tempo and simultaneous speaking, remains a critical and unevaluated challenge for conversational fluency. To address this gap, we introduce the Game-Time Benchmark, a framework to systematically assess these temporal capabilities. Inspired by how humans learn a language through language activities, Game-Time consists of basic instruction-following tasks and advanced tasks with temporal constraints, such as tempo adherence and synchronized responses. Our evaluation of diverse SLM architectures reveals a clear performance disparity: while state-of-the-art models handle basic tasks well, many contemporary systems still struggle with fundamental instruction-following. More critically, nearly all models degrade substantially under temporal constraints, exposing persistent weaknesses in time awareness and full-duplex interaction. The Game-Time Benchmark provides a foundation for guiding future research toward more temporally-aware conversational AI. Demos and datasets are available on our project website https://ga642381.github.io/Game-Time.

Game-Time：評估語音語言模型中的時間動態特性

Game-Time: Evaluating Temporal Dynamics in Spoken Language Models

摘要

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