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

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.