Game-Time: Evaluating Temporal Dynamics in Spoken Language Models

📰 ArXiv cs.AI

Learn to evaluate temporal dynamics in spoken language models with the Game-Time Benchmark

advanced Published 28 Apr 2026

Action Steps

Apply the Game-Time Benchmark to assess temporal dynamics in SLMs
Evaluate the ability of SLMs to manage timing and tempo
Test SLMs for simultaneous speaking capabilities
Analyze the results to identify areas for improvement
Configure SLMs to optimize temporal dynamics for real-time speech interaction

Who Needs to Know This

NLP engineers and researchers can benefit from this framework to improve conversational fluency in spoken language models

Key Insight

💡 Temporal dynamics are crucial for conversational fluency in spoken language models

Key Takeaways

Learn to evaluate temporal dynamics in spoken language models with the Game-Time Benchmark

Full Article

Title: Game-Time: Evaluating Temporal Dynamics in Spoken Language Models

Abstract:
arXiv:2509.26388v3 Announce Type: replace-cross 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

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