{"ID":2862858,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.26388","arxiv_id":"2509.26388","title":"Game-Time: Evaluating Temporal Dynamics in Spoken Language Models","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.","short_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 t...","url_abs":"https://arxiv.org/abs/2509.26388","url_pdf":"https://arxiv.org/pdf/2509.26388v4","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\"]","published":"2025-09-30T15:23:39Z","proceeding":"eess.AS","tasks":"[\"eess.AS\",\"cs.AI\",\"cs.CL\"]","methods":"[\"Language Model\"]","has_code":false}