Machine Translation · বাংলা
Bengali Data for Machine Translation
Training and evaluating translation between English and Indian languages, and between Indian languages. In Bengali, the binding constraint is usually dialect coverage and code-mixing, not raw hours.
- Language
- Bengali
- Primary metric
- Human adequacy and fluency scores
- Typical volume
- 500-2,000 hours
Data profile required
- Sentence-aligned parallel corpora
- Register-matched to your product
- Enforced terminology glossary
What Bengali adds to the requirement
- Inherent vowel is realised as /ɔ/ or /o/, which breaks G2P rules copied from Devanagari-based systems
- No phonemic distinction between শ ষ স in most speech despite three orthographic characters
- Dialects to cover: Kolkata standard (Rarhi), Sylheti-influenced, Rangpuri / North Bengal, Medinipuri
- Kolkata professional speech mixes English heavily; rural West Bengal much less. A single 'Bengali' dataset without register tags conflates two very different acoustic and lexical distributions.
Metrics to track
- Human adequacy and fluency scores
- Terminology compliance rate
- Back-translation divergence
Failure modes
- Pivoting everything through English
- Post-edited machine output passed off as human translation
For Bengali specifically: Indian Bengali is under-collected relative to Bangladeshi Bengali, and North Bengal and Tripura varieties are almost entirely missing.
Recommended cohort
Tag every speaker as Indian Bengali and record district of origin; mixing in Bangladeshi speech without tags is a common and costly dataset defect.
| Dimension | Typical split | Why it matters for Bengali |
|---|---|---|
| Gender | 50 / 50 | Pitch range differences change acoustic model behaviour; unbalanced cohorts bias recognition |
| Age | 18-25: 30%, 26-40: 40%, 41-60: 30% | Older speakers retain conservative Bengali forms that younger urban speakers have lost |
| Region | West Bengal / Tripura / Assam (Barak Valley) and others | Dialect spread across 5 recognised varieties |
| Education | Mixed, including below-graduate | Prompt-reading fluency correlates with education and skews prosody |
| Condition | Studio / quiet room / field | Match the noise profile of your deployment |
Suggested programme shape
Start with an evaluation set of 100 speakers spread across every Bengali dialect in scope, collected before training data. Then field 500-2,000 hours of training data from disjoint speakers.
This ordering is what makes the improvement measurable rather than assumed.
Frequently asked
Is there usable public Bengali data for machine translation?
Indian Bengali is under-collected relative to Bangladeshi Bengali, and North Bengal and Tripura varieties are almost entirely missing.
How many Bengali speakers do we need?
1,000-3,000 speakers for a training corpus, plus a disjoint evaluation cohort covering each dialect. Speaker count matters more than hours for generalisation.
Can you run this across multiple languages at once?
Yes. Multi-language programmes run to one master specification so per-language results stay comparable.
Scope Bengali data for machine translation
Send the target metric and the languages in scope.