LLM human data · Indian English
Indian English Human Data for LLM Projects
Human-generated text and speech for LLM training and evaluation in Indian languages: prompts, preference rankings, instruction-response pairs, red-teaming and cultural-fit review. This page covers how that works specifically for Indian English, where retroflex realisation of /t/ and /d/.
- Language
- Indian English (en-IN)
- Dialects covered
- 5
- Typical programme
- 500-2,000 hours
- Cities
- Bengaluru, Delhi, Mumbai
What changes when the language is Indian English
The service specification stays constant across languages; the linguistics do not. For Indian English, three things drive the design of a llm human data programme.
- Retroflex realisation of /t/ and /d/
- Dialect spread: North Indian (Hindi-substrate), Maharashtrian, South Indian (Tamil/Telugu/Kannada/Malayalam substrate), Bengali-substrate
- Indian English embeds Hindi and regional discourse markers, kinship terms, and food and place vocabulary that Western English lexicons lack.
Technical specification
| Parameter | Standard |
|---|---|
| Task types | Prompt writing, response ranking, instruction-response pairs, adversarial testing |
| Languages | Any language in the network, including code-mixed Hinglish |
| Contributors | Screened by domain, education band and language proficiency |
| Agreement | Overlapping assignments with adjudication |
| Provenance | Per-item contributor and time records |
Indian English cohort design
Balance by substrate language, not by city alone, and tag each speaker so accent-band evaluation is possible after delivery.
| Dimension | Typical split | Why it matters for Indian English |
|---|---|---|
| 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 Indian English forms that younger urban speakers have lost |
| Region | Pan-India, with distinct regional accent bands 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 |
Process
- Task specification and rubric design
- Contributor screening against the rubric
- Calibration round with feedback
- Production with overlap and gold items
- Adjudication and delivery
Indian English-specific quality rules
- Indian-specific vocabulary flagged as errors by spellcheck-driven QA
- Numbers spoken in lakhs and crores mis-normalised into millions
- Indian address and name spelling requires a domain-specific style guide
Every item is traceable to a screened contributor, which matters when a model vendor audits your data provenance.
Deliverables
- Task data in your schema
- Rubric and calibration results
- Contributor metadata (anonymised)
- Agreement statistics
Worked example
A representative Indian English llm human data engagement: 500 hours from 1,000 speakers, 50/50 gender, ages 18-45, spread across Bengaluru, Delhi, Mumbai, recorded to the specification above and delivered in WAV with a per-utterance manifest.
Timeline: 2-6 weeks depending on task complexity and contributor screening depth.
Where this data is missing today
Commercial English ASR is trained overwhelmingly on US and UK speech. Indian English accent data with substrate-language tagging is the fastest way to close the accuracy gap for Indian deployments.
Frequently asked
How much does Indian English llm human data cost?
Priced per delivered hour or unit against a written spec. The cost drivers for Indian English are dialect spread, demographic narrowness and recording condition, in that order.
Which Indian English dialects are included?
By default North Indian (Hindi-substrate), Maharashtrian, South Indian (Tamil/Telugu/Kannada/Malayalam substrate), Bengali-substrate and others, tagged per speaker. You can also commission a single-dialect corpus if you are targeting one region.
Can you deliver Indian English data in our format?
Yes. Task data in your schema is the default, but naming, schema and directory structure follow your pipeline.
How long does a Indian English programme take?
2-6 weeks depending on task complexity and contributor screening depth.
Request a Indian English llm human data quote
Hours, speakers, dialects, deadline. Send what you have.