VAD vs event-triggered for AI speech-to-speech applications
Building natural, real-time speech-to-speech AI requires more than high-quality transcription and synthesis. The system must also understand when a person is actually speaking. Determining that boundary distinguishing meaningful speech from breathing, shuffling papers, or background noise shapes the entire user experience. Two main strategies dominate modern implementations: Voice Activity Detection (VAD) and event-triggered control . Both offer advantages, and both introduce trade-offs. Understanding when to use each approach is key to designing responsive, human-like conversational systems. What Voice Activity Detection Actually Does At its core, Voice Activity Detection listens continuously and decides whether incoming audio contains human speech. Effective VAD filters raw audio with techniques like hangover timers and minimum-duration rules, reducing false positives from short noises or spikes. When implemented well, VAD improves: – Latency – Compute efficiency – Detecti...