ASR datasets · Indian English
Indian English ASR Training Data
Transcribed speech corpora built to train and evaluate automatic speech recognition, with verbatim transcription, timestamps and per-token language tagging where code-mixing occurs. 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 asr datasets 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 |
|---|---|
| Audio | 16 kHz or 48 kHz PCM WAV |
| Transcription | Verbatim, including disfluencies, false starts and fillers |
| Timestamps | Utterance level by default; word level on request |
| Tagging | Noise, overlap, unintelligible, foreign-language and code-switch tags |
| Normalisation | Raw and normalised text columns delivered separately |
| Split | Train/dev/test splits with no speaker leakage across splits |
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
- Style guide authored per language, covering numerals, loanwords, script and disfluency rules
- Transcriber calibration round with inter-annotator agreement measurement
- First-pass transcription
- Second-pass native review
- Automated consistency checks against the style guide
- Split generation with speaker-disjoint partitions
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
Agreement is measured, not assumed. We report word-level agreement on a held-out sample so you can judge label quality before training on it.
Deliverables
- Audio plus aligned transcripts (JSON/TSV, or your schema)
- Style guide as delivered documentation
- Inter-annotator agreement report
- Speaker-disjoint train/dev/test splits
Worked example
A representative Indian English asr datasets 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: Transcription adds roughly 1-2 weeks per 100 hours after recording, per language.
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 asr datasets 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. Audio plus aligned transcripts (JSON/TSV, or your schema) is the default, but naming, schema and directory structure follow your pipeline.
How long does a Indian English programme take?
Transcription adds roughly 1-2 weeks per 100 hours after recording, per language.
Request a Indian English asr datasets quote
Hours, speakers, dialects, deadline. Send what you have.