AI Companies · Wake Word Detection
Wake Word Detection Data for AI Companies
Product and platform teams that need Indian-language training data on a schedule that matches their model release cycle, not a vendor's studio availability. Training and hardening a device wake word against Indian phonetics, background noise and near-miss phrases.
- Buyer
- AI Companies
- Use case
- Wake Word Detection
- Metric
- False accepts per hour
Where the two meet
Model accuracy collapses on Indian accents and languages that were absent from the pretraining mix 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
- Can the partner field the speaker count and demographic quotas exactly as written?
- Is consent documented per speaker and mapped to file IDs?
- Are QA thresholds measurable and reported, or asserted?
- Can delivery be staged so training can start before the full corpus lands?
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
- Perpetual, transferable licence to the delivered data
- Clear IP assignment
- Consent that survives model distribution
- Data residency and handling
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.