Echo Models: Linguistic Turn Detection for Voice Agents

Echo Turn Detection is a server-hosted, real-time conversational timing layer for voice agents that uses streaming transcript linguistics, hesitation cues, punctuation, and intent rather than fixed silence thresholds to determine when an agent should respond, continue listening, ignore acknowledgments, or pause. It addresses common voice-agent failures including premature interruptions, delayed replies, mistaken backchannels, and failure to stop when asked, through four classifications: complete, incomplete, backchannel, and wait. Available through VideoSDK’s Inference Gateway in low-latency Echo Small and higher-accuracy Echo Large variants, it supports 12 languages and integrates into pipelines alongside voice activity detection, speech-to-text, language models, and text-to-speech. On the English TURNS2K benchmark, Echo Large reportedly reached 96.2% overall accuracy and 96.5% recall for completed turns, while Echo Small achieved 97.3% completion recall, compared with a baseline’s 32.8% completion recall. The service is accessible through the TurnV2 class in the videosdk-agents Python library, with the intended benefits of reducing response latency, improving conversational naturalness, and avoiding unnecessary language-model calls.

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