What is Voice Activity Detection

How It Works

Voice activity detection analyzes audio frames (typically 20-30ms windows) to classify each frame as speech or non-speech. Traditional methods use energy levels, spectral features, and statistical models. Modern neural VAD models process longer context windows and learn discriminative features that distinguish speech from various noise types, producing binary speech/non-speech labels with timestamps.

Technical Details

State-of-the-art models include Silero VAD (lightweight, fast), pyannote VAD (neural, accurate), and WebRTC VAD (rule-based, ultra-fast). Output is a list of speech segments with start and end timestamps. Models operate on 16kHz mono audio and produce decisions at 10-30ms resolution. Metrics include false alarm rate, missed detection rate, and overall accuracy. Processing speed ranges from 50x to 500x real-time depending on model complexity.

Best Practices

• Apply VAD before speech recognition to skip non-speech segments and reduce processing cost
• Use neural VAD models for noisy environments and rule-based models for clean audio
• Tune sensitivity thresholds based on whether false alarms or missed speech is more costly
• Add minimum duration filters to prevent rapid switching between speech and non-speech

Common Pitfalls

• Not accounting for music or environmental sounds that can be misclassified as speech
• Using overly aggressive VAD that clips the beginnings and endings of speech segments
• Applying VAD trained on close-talk microphone data to far-field or reverberant audio
• Not smoothing VAD output, resulting in many very short speech and non-speech segments

Advanced Tips

• Use VAD to create a speech activity timeline for efficient navigation of long recordings
• Combine VAD with speaker diarization for speaker-attributed speech segments
• Implement streaming VAD for real-time speech detection in live audio applications
• Use VAD confidence scores for soft decisions rather than hard binary speech/non-speech labels