LiveKit Turn Detector An open weights language model for contextually aware end of utterance (EOU) detection in voice AI applications. The model predicts whether a user has finished speaking based on the semantic content of their transcribed speech, providing a critical complement to voice activity detection (VAD) systems. 📖 For installation, usage examples, and integration guides, see the LiveKit documentation. Table of Contents Overview Model Variants How It Works Architecture and Training Supported Languages Benchmarks Usage Deployment Requirements Limitations License Resources Overview Traditional voice agents rely on voice activity detection (VAD) to determine when a user has finished speaking. VAD works by detecting the presence or absence of speech in an audio signal and applying a silence timer. While effective for detecting pauses, VAD lacks language understanding and frequently causes false positives. For example, a user who says "I need to think about that for a moment..." and then pauses will be interrupted by a VAD only system, even though they clearly intend to continue. This model adds semantic understanding to the turn detection process. It analyzes the transcribed…
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