Game-Time: Evaluating Temporal Dynamics in Spoken Language Models

• URL: http://arxiv.org/abs/2509.26388v1
• Date: Tue, 30 Sep 2025 15:23:39 GMT
• Title: Game-Time: Evaluating Temporal Dynamics in Spoken Language Models
• Authors: Kai-Wei Chang, En-Pei Hu, Chun-Yi Kuan, Wenze Ren, Wei-Chih Chen, Guan-Ting Lin, Yu Tsao, Shao-Hua Sun, Hung-yi Lee, James Glass,
• Abstract summary: We introduce the Game-Time Benchmark framework to assess temporal capabilities.<n>Our evaluation of diverse SLM models reveals a clear performance disparity.<n>The GameTime Benchmark provides a foundation for guiding future research toward more temporally-aware conversational AI.
• Score: 93.844257719952
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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