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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.26388 |
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| _version_ | 1866917453279264768 |
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| author | Chang, Kai-Wei Hu, En-Pei Kuan, Chun-Yi Ren, Wenze Chen, Wei-Chih Lin, Guan-Ting Tsao, Yu Sun, Shao-Hua Lee, Hung-yi Glass, James |
| author_facet | Chang, Kai-Wei Hu, En-Pei Kuan, Chun-Yi Ren, Wenze Chen, Wei-Chih Lin, Guan-Ting Tsao, Yu Sun, Shao-Hua Lee, Hung-yi Glass, James |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26388 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Game-Time: Evaluating Temporal Dynamics in Spoken Language Models Chang, Kai-Wei Hu, En-Pei Kuan, Chun-Yi Ren, Wenze Chen, Wei-Chih Lin, Guan-Ting Tsao, Yu Sun, Shao-Hua Lee, Hung-yi Glass, James Audio and Speech Processing Artificial Intelligence Computation and Language 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. |
| title | Game-Time: Evaluating Temporal Dynamics in Spoken Language Models |
| topic | Audio and Speech Processing Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2509.26388 |