Call Centre AI Companies · ASR Model Training
ASR Model Training 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. Building or fine-tuning speech recognition for Indian languages from scratch or from a multilingual base model.
- Buyer
- Call Centre AI Companies
- Use case
- ASR Model Training
- Metric
- Word error rate overall and per dialect
Where the two meet
Production audio is 8 kHz telephony; models trained on studio audio degrade sharply That is a asr model training problem, and it is solved by data shaped like this:
- Hundreds to thousands of hours of verbatim-transcribed speech
- Wide speaker diversity: age, gender, region, education, recording condition
- Speaker-disjoint train/dev/test splits
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
- Word error rate overall and per dialect
- Entity error rate on names and numbers
- Code-switch token accuracy
Pitfalls
- Read-speech-only corpora that do not transfer to spontaneous audio
- Speaker leakage across splits inflating reported accuracy
- Normalised-only transcripts with the raw text discarded
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.