ML Research Groups · Wake Word Detection
Wake Word Detection Data for ML Research Groups
Academic and industrial research teams building benchmarks and studying low-resource Indian languages, where documentation and reproducibility matter as much as volume. Training and hardening a device wake word against Indian phonetics, background noise and near-miss phrases.
- Buyer
- ML Research Groups
- Use case
- Wake Word Detection
- Metric
- False accepts per hour
Where the two meet
Low-resource languages have no usable public data at all That is a wake word detection problem, and it is solved by data shaped like this:
- Thousands of speakers, few utterances each
- Positive and hard-negative sets
- Multiple distances and noise conditions
Your evaluation criteria
- Is the collection protocol documented well enough to publish?
- Are speaker demographics reported in aggregate for dataset cards?
- Can the data be released openly, and under what consent terms?
Metrics
- False accepts per hour
- False reject rate per accent band
- Performance at 3m and 5m
Pitfalls
- Positives only, with no hard negatives
- Close-mic-only capture
- No accent-band tagging, so failures cannot be localised
Contract points
- Open-release-compatible consent
- Dataset card material provided with delivery
- Attribution and citation terms
Frequently asked
What does a first engagement look like?
Usually a scoped pilot: one language, an evaluation set plus a first training batch, delivered in three to five weeks, followed by the full programme.
Can you match our existing vendor's schema?
Yes. Working to your schema avoids a conversion pass and keeps deliveries comparable across vendors.
How is provenance documented?
Per-item contributor records and consent mapped to IDs in the manifest.
Send your requirement
Language, volume, metric, deadline.