[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-voice-activity-detection::en":3,"gloss-cluster-voice-activity-detection::en":20,"gloss-next-voice-activity-detection::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"voice-activity-detection","output","Voice Activity Detection","Voice activity detection (VAD) is a classification model that identifies which segments of an audio stream contain human speech versus silence, background noise, or non-speech sound, typically operating as a lightweight, low-latency preprocessing step rather than a standalone end-user-facing output — its job is to tell a downstream pipeline exactly when speech starts and stops. Modern VAD (Silero VAD, WebRTC VAD, and the endpointing logic built into most STT\u002Fvoice-agent SDKs) uses a small, fast neural classifier operating on short audio frames (often 10-30ms windows) to output a binary or probabilistic speech\u002Fno-speech signal in real time, cheap enough to run continuously on a mobile device or embedded system without noticeable battery drain or added latency, which matters because VAD typically runs as an always-on, continuously-listening background process rather than an occasional, on-demand API call triggered by a button press. Why it matters for SaaS builders: VAD is invisible, unglamorous infrastructure that nonetheless determines the perceived quality of nearly every voice product — a voice AI agent needs accurate VAD to know when the user has finished speaking and it's the agent's turn to respond (\"endpointing\"), without which the agent either interrupts the user mid-sentence (VAD triggers too early) or leaves an awkward, expensive silence waiting for more audio that isn't coming (VAD triggers too late); transcription and meeting-recording tools use VAD to trim silence and save on STT API costs (many providers bill per second of audio processed, so stripping silent gaps before sending audio to the STT API directly reduces cost); and call-center\u002FIVR systems use VAD to detect when a caller has stopped talking to advance a scripted flow. A concrete worked example — a voice AI agent's turn-taking logic: (1) as the user speaks into a live voice-agent call, a VAD model runs continuously on the incoming audio stream, frame by frame; (2) once VAD detects 700ms of continuous silence following detected speech, the pipeline treats that as the end of the user's turn and forwards the buffered audio to the STT model for transcription; (3) the resulting text is sent to the LLM for a response, which is spoken back via TTS; (4) tuning the silence threshold is a real product decision — too short and the agent cuts off users who pause mid-thought, too long and the conversation feels sluggish, so many production systems make it adaptive based on the user's observed speaking cadence.","Voice activity detection (VAD) identifies which segments of an audio stream contain human speech versus silence or background noise.",null,[11,14,17],{"slug":12,"name":13},"speech-to-text","Speech-to-Text (STT)",{"slug":15,"name":16},"text-to-speech","Text-to-Speech (TTS)",{"slug":18,"name":19},"voice-synthesis","Voice Synthesis",[21,25,29,33,36,40,43,46,49,52,55,58],{"slug":22,"category":5,"name":23,"updated_at":24},"abstention","Abstention","2026-08-24T03:30:02+00:00",{"slug":26,"category":5,"name":27,"updated_at":28},"ai-copywriting","AI Copywriting","2026-08-24T02:46:38+00:00",{"slug":30,"category":5,"name":31,"updated_at":32},"ai-watermarking","AI Watermarking","2026-08-24T02:46:37+00:00",{"slug":34,"category":5,"name":35,"updated_at":32},"aspect-ratio-control","Aspect-Ratio Control",{"slug":37,"category":5,"name":38,"updated_at":39},"audio-generation","Audio Generation","2026-08-24T02:46:36+00:00",{"slug":41,"category":5,"name":42,"updated_at":32},"audio-super-resolution","Audio Super-Resolution",{"slug":44,"category":5,"name":45,"updated_at":39},"avatar-generation","Avatar Generation",{"slug":47,"category":5,"name":48,"updated_at":39},"background-removal","Background Removal",{"slug":50,"category":5,"name":51,"updated_at":32},"batch-image-generation","Batch Image Generation",{"slug":53,"category":5,"name":54,"updated_at":28},"brand-voice","Brand Voice",{"slug":56,"category":5,"name":57,"updated_at":28},"cfg-scale","CFG Scale (Classifier-Free Guidance)",{"slug":59,"category":5,"name":60,"updated_at":32},"character-consistency","Character Consistency"]