Call Centre AI Companies · Wake Word Detection
Wake Word Detection Data for Call Centre AI Companies
Agent-assist, QA-automation and voice-bot vendors serving Indian BPO and enterprise contact centres, working with narrowband telephony audio and heavy accent variation. Training and hardening a device wake word against Indian phonetics, background noise and near-miss phrases.
- Buyer
- Call Centre AI Companies
- Use case
- Wake Word Detection
- Metric
- False accepts per hour
Where the two meet
Production audio is 8 kHz telephony; models trained on studio audio degrade sharply 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 narrowband simulated at capture, not by downsampling studio audio?
- Are agent and customer on separate channels?
- Are emotion and escalation variants available on demand?
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
- Consented synthetic-scenario audio with no real customer PII
- Scenario library ownership
- Per-scenario volume guarantees
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.