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Quail Voice Focus is designed for voice agents that should follow one primary speaker. Evaluate it with your speech-to-text (STT) provider to determine whether suppressing competing speech improves the behavior your application needs. Use the paired audio evaluation procedure to establish a baseline, then rerun the same recordings after each tuning change. ProcessorParameter.EnhancementLevel accepts values from 0.0 to 1.0. Higher values request stronger suppression. The starting value comes from the loaded model; read it through processor.get_context().get_parameter(...) in Python or processor.getContext().getParameter(...) in Node.js.
Voice Focus makes suppression decisions from audio context. Speech can be ambiguous, especially when speakers overlap or the target is quiet. Increasing the enhancement level changes suppression strength; it does not guarantee that every foreground word will remain or that every background voice will disappear.
Use these as comparison points for an evaluation, not measured quality rankings: Keep the input recordings, STT version and decoding settings fixed. Change one setting at a time. No setting guarantees preservation of all foreground speech, and the best result can differ by provider and acoustic condition.

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Best practices

  • Match the target-speaker task. Use Voice Focus when background speech should be suppressed. Use Quail’s multi-speaker models when all participants matter.
  • Measure insertions and deletions separately. Lower overall word error rate can hide loss of a critical target utterance.
  • Include startup and retargeting. Test the beginning of a stream, quiet speech and a change in the most prominent speaker. Preserve state between consecutive frames.
  • Check agent behavior. Compare false interruptions, missed requests and end-of-turn latency alongside transcription quality.
Use the model reference to record the exact model ID and the performance guide for reproducible resource measurements.