LLM Companies · Wake Word Detection
Wake Word Detection Data for LLM Companies
Foundation and applied LLM teams that need Indian-language human data with provable provenance, covering languages their web crawl barely touched. Training and hardening a device wake word against Indian phonetics, background noise and near-miss phrases.
- Buyer
- LLM Companies
- Use case
- Wake Word Detection
- Metric
- False accepts per hour
Where the two meet
Web-scraped Indian-language text is thin, noisy and heavily transliterated 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 every item traceable to a screened, consenting contributor?
- Can contributors be screened by domain expertise, not just language?
- Is there an adjudication process for disagreement on subjective tasks?
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
- Auditable provenance records
- Contributor consent for model training and distribution
- No third-party or scraped content in deliverables
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.