LLM Companies · Code-Switching ASR
Code-Switching ASR 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. Recognising speech that switches between an Indian language and English several times per sentence.
- Buyer
- LLM Companies
- Use case
- Code-Switching ASR
- Metric
- Switch-point accuracy
Where the two meet
Web-scraped Indian-language text is thin, noisy and heavily transliterated 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 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
- 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
- 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.