Indian English · Latin · en-IN
Indian English AI Training Data Collection
We collect Indian English speech and language data for AI training across Pan-India, with distinct regional accent bands and beyond, covering 5 dialect varieties rather than a single prestige standard.
- Speakers
- 130M
- Script
- Latin
- Studio cities
- 7
- Typical programme
- 500-2,000 hours
Why Indian English breaks generic speech models
Indian English is not a variant of a language your model already handles. The specific properties below are the ones that show up as errors in production.
- Retroflex realisation of /t/ and /d/
- Monophthongal /e/ and /o/ where US English has diphthongs
- Syllable-timed rather than stress-timed rhythm, which breaks duration models trained on native English
- /v/-/w/ merger in several substrate groups
Dialects we cover
Indian English has 5 varieties that matter for data collection. Collecting only the standard variety produces a model that works in the capital and fails everywhere else.
- North Indian (Hindi-substrate)
- Maharashtrian
- South Indian (Tamil/Telugu/Kannada/Malayalam substrate)
- Bengali-substrate
- North-East
Code-mixing reality
Indian English embeds Hindi and regional discourse markers, kinship terms, and food and place vocabulary that Western English lexicons lack.
Specify your expected English-token ratio in the brief. It is cheaper to collect the right mix than to filter the wrong one afterwards.
What is missing from public data
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.
This is the practical reason to commission collection rather than assemble open corpora: the gap in the public data is exactly the part your users occupy.
Transcription rules that decide whether the data is usable
These are the Indian English-specific decisions that go into the style guide before any transcriber starts work.
- 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
Recording types available
- Accent-band balanced read speech with substrate tags
- Business and support calls in English
- Indian names, addresses, PIN codes and lakh/crore amounts
Recommended cohort structure
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 |
Where we record
Indian English sessions run in Bengaluru, Delhi, Mumbai, Chennai, Hyderabad, Kolkata, Pune. Multi-city fielding is the default because single-city cohorts carry an audible accent signature.
The numbers we hold ourselves to
- 100% of delivered files pass automated technical QA for SNR, clipping, duration and silence
- 5-25% of files pass a second native-speaker content review, stratified by city, dialect and transcriber, and escalating to 100% on any batch that fails the agreed threshold
- Accepted yield runs 85-90% for scripted speech, 60-70% for spontaneous, 55-65% for conversational and 50-60% for telephony
- Default cohort quotas: 50/50 gender, with age bands at 30% (18-25), 40% (26-40) and 30% (41-60)
- 48 kHz / 24-bit capture, delivered as 16-bit PCM WAV, with studio sessions held below a -50 dBFS noise floor
- First response within one working day; a scoped, fixed quote within two to three
These are the figures a delivery is measured against, not aspirations. A batch that misses them is re-recorded at our cost rather than repaired.
Frequently asked
How many Indian English speakers can you field?
1,000-3,000 speakers for a standard programme, fielded across 7 cities. Balance by substrate language, not by city alone, and tag each speaker so accent-band evaluation is possible after delivery.
Do you transcribe Indian English in Latin?
Yes, and we can deliver romanised or dual-script transcripts alongside. Indian-specific vocabulary flagged as errors by spellcheck-driven QA is one of the rules fixed in the style guide before work begins.
Can you collect dialect-specific Indian English data?
Yes. Every speaker is tagged with region and dialect, so you can evaluate per variety after delivery instead of discovering the imbalance in production.
What about Indian English-English code-mixing?
Indian English embeds Hindi and regional discourse markers, kinship terms, and food and place vocabulary that Western English lexicons lack.
Request a Indian English dataset quote
Tell us the hours, speaker count and dialect spread you need. You get a scope, a timeline and a price.