Speech AI Companies · Machine Translation
Machine Translation Data for Speech AI Companies
Teams whose core product is speech recognition or synthesis, where dataset quality is the product roadmap and word error rate is the metric everyone watches. Training and evaluating translation between English and Indian languages, and between Indian languages.
- Buyer
- Speech AI Companies
- Use case
- Machine Translation
- Metric
- Human adequacy and fluency scores
Where the two meet
WER on Indian languages is dominated by dialect and code-mixing failures that generic corpora do not cover That is a machine translation problem, and it is solved by data shaped like this:
- Sentence-aligned parallel corpora
- Register-matched to your product
- Enforced terminology glossary
Your evaluation criteria
- Are train/dev/test splits speaker-disjoint by construction?
- Is transcription verbatim, with disfluencies preserved?
- Is per-token language ID available for code-mixed speech?
- Is inter-annotator agreement measured and reported?
Metrics
- Human adequacy and fluency scores
- Terminology compliance rate
- Back-translation divergence
Pitfalls
- Pivoting everything through English
- Post-edited machine output passed off as human translation
Contract points
- Speaker-disjoint splits guaranteed contractually
- Right to publish benchmark results
- Re-record remedy for QA failures
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.