Threshold & sliding window

The model emits a score for each inference window. Two settings turn that noisy stream into a detection.

Probability cutoff

probability_cutoff ranges from 0.0 to 1.0. A detection requires the smoothed probability to reach this value.

Lower cutoff	Higher cutoff
More sensitive	More selective
Can hear weaker pronunciations	Rejects more uncertain audio
More false activations	More missed or difficult activations

# use micro_wakeword::Detector;
# fn run() -> micro_wakeword::Result<()> {
let detector = Detector::builder("model.tflite")
    .wake_word("hello")
    .probability_cutoff(0.42)
    .build()?;
# Ok(()) }

Sliding-window size

The detector averages the latest model probabilities. sliding_window_size controls how many are included.

Smaller window	Larger window
Reacts more quickly	Produces a steadier score
More affected by brief spikes	Requires confidence to persist
May increase false activations	May add latency or miss short peaks

It must be at least 1.

Tune them together

A threshold does not have the same meaning under every window size. Changing smoothing changes the score distribution, so evaluate combinations rather than choosing each setting independently.

1. Start with the model author’s values.
2. Build representative positive and negative recordings.
3. Measure missed wake words and false activations separately.
4. Adjust one combination at a time.
5. Validate on microphones, speakers, accents, distances, and noise not used during tuning.

The feature_step_size_ms must remain 10; the crate’s frontend and streaming contract are designed around ten-millisecond steps.