Voice Activity Detection (VAD)

Voice Activity Detection (VAD) is the algorithmic process that determines when human speech is present in an audio stream, distinguishing it from silence, background noise, and non-speech sounds. Modern VAD systems use deep learning models trained on millions of hours of audio to achieve 95%+ accuracy in challenging acoustic environments. VAD serves as the gatekeeper for voice agents, controlling when to listen, when to process, and when to respond. The technology has evolved from simple energy-based detectors to sophisticated neural networks that can identify speech in noisy environments, handle multiple speakers, and adapt to different languages and accents.

VAD accuracy directly impacts every aspect of voice agent performance. False positives (detecting speech when there is none) waste computational resources and can trigger inappropriate responses to background noise. False negatives (missing actual speech) cause users to repeat themselves, leading to frustration and abandoned calls. Poor VAD is the leading cause of interruption issues, where agents cut off users mid-sentence or wait too long after they've finished. Studies show that VAD errors account for 40% of user complaints in voice applications. Proper VAD tuning can reduce average handle time by 15-20% by eliminating unnecessary pauses and repetitions.

Modern VAD systems use multi-stage processing pipelines. First, acoustic features like energy, zero-crossing rate, and spectral characteristics are extracted from audio frames (typically 10-30ms). These features feed into a neural network classifier, often a lightweight CNN or RNN trained to recognize speech patterns. The classifier outputs probability scores that are smoothed using techniques like hangover periods and median filtering to avoid rapid switching. Advanced VADs incorporate context awareness, adjusting sensitivity based on conversation state and expected turn-taking patterns. Many systems use WebRTC's VAD as a preliminary filter, then apply more sophisticated models for final decisions. Adaptive thresholds adjust for background noise levels, while specialized models handle challenging cases like whispered speech or heavily accented English.

Hamming AI's testing reveals that poor VAD configuration is a leading cause of user interruptions and conversation flow issues. Common problems include: VAD too aggressive (cuts off users mid-sentence), VAD too conservative (long awkward pauses), and failure to handle background noise. Their analytics platform tracks 'user interruption' metrics specifically to identify VAD issues. Testing should include recordings with background noise and diverse accents to validate VAD performance across real-world conditions. Source: https://hamming.ai/blog/call-analytics-voice-agent-testing

Start with established VAD libraries like WebRTC VAD, Silero VAD, or py-webrtcvad, then tune parameters for your specific use case. Set different thresholds for speech onset (more aggressive) versus offset (more conservative) to balance responsiveness with completion. Implement adaptive thresholds that adjust based on measured noise floors. Use comfort noise generation during silence periods to avoid dead air. Add specialized handling for known problematic scenarios like background TV or music. Monitor VAD performance metrics: false positive rate, false negative rate, and average speech/silence segment duration. Consider implementing multiple VAD models in parallel and using voting or confidence weighting for critical decisions. Test extensively with real-world audio including various accents, ages, and acoustic environments.