New AI Framework Improves Speech Chatbot Turn Detection Efficiency

A novel collaborative inference framework called SpeculativeETD has been introduced to enhance end-turn detection in spoken dialogue systems. This approach addresses the persistent challenge where large language model-powered chatbots frequently misjudge when users have finished speaking, resulting in premature or delayed responses that disrupt conversation flow. The framework employs a lightweight GRU-based model to quickly identify non-speaking units within real-time audio streams. To support this development, researchers have created the ETD Dataset, marking the first publicly available resource specifically for end-turn detection training and evaluation. This dataset incorporates both synthetic speech generated through text-to-speech models and authentic speech collected from various web sources. The methodology is designed to balance computational efficiency with detection accuracy, making it particularly suitable for deployment in environments with limited processing resources. The research was documented in the paper arXiv:2503.23439v2, which was announced as a replacement cross on the arXiv preprint server.