Call Centre AI Companies · Code-Switching ASR
Code-Switching ASR 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. Recognising speech that switches between an Indian language and English several times per sentence.
- Buyer
- Call Centre AI Companies
- Use case
- Code-Switching ASR
- Metric
- Switch-point accuracy
Where the two meet
Production audio is 8 kHz telephony; models trained on studio audio degrade sharply That is a code-switching asr problem, and it is solved by data shaped like this:
- Genuinely code-mixed spontaneous speech
- Per-token language ID labels
- A fixed rule for script of English tokens
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
- Switch-point accuracy
- Mixed-utterance WER
- Language ID token accuracy
Pitfalls
- Concatenating monolingual data and calling it code-mixed
- Leaving script conventions to individual annotators
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.