Thai Semantic End-of-Turn Detection for Real-Time Voice Agents

• URL: http://arxiv.org/abs/2510.04016v1
• Date: Sun, 05 Oct 2025 03:31:59 GMT
• Title: Thai Semantic End-of-Turn Detection for Real-Time Voice Agents
• Abstract summary: Traditional audio-silence end-pointers add hundreds of milliseconds of delay and fail under hesitations or language-specific phenomena.<n>We present to our knowledge, the first systematic study of Thai text-only end-of-turn (EOT) detection for real-time agents.
• Score: 0.3499870393443268
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fluid voice-to-voice interaction requires reliable and low-latency detection of when a user has finished speaking. Traditional audio-silence end-pointers add hundreds of milliseconds of delay and fail under hesitations or language-specific phenomena. We present, to our knowledge, the first systematic study of Thai text-only end-of-turn (EOT) detection for real-time agents. We compare zero-shot and few-shot prompting of compact LLMs to supervised fine-tuning of lightweight transformers. Using transcribed subtitles from the YODAS corpus and Thai-specific linguistic cues (e.g., sentence-final particles), we formulate EOT as a binary decision over token boundaries. We report a clear accuracy-latency tradeoff and provide a public-ready implementation plan. This work establishes a Thai baseline and demonstrates that small, fine-tuned models can deliver near-instant EOT decisions suitable for on-device agents.

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