Code-Switching ASR · Indian English
Indian English Data for Code-Switching ASR
Recognising speech that switches between an Indian language and English several times per sentence. In Indian English, the binding constraint is usually dialect coverage and code-mixing, not raw hours.
- Language
- Indian English
- Primary metric
- Switch-point accuracy
- Typical volume
- 500-2,000 hours
Data profile required
- Genuinely code-mixed spontaneous speech
- Per-token language ID labels
- A fixed rule for script of English tokens
What Indian English adds to the requirement
- Retroflex realisation of /t/ and /d/
- Monophthongal /e/ and /o/ where US English has diphthongs
- Dialects to cover: 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.
Metrics to track
- Switch-point accuracy
- Mixed-utterance WER
- Language ID token accuracy
Failure modes
- Concatenating monolingual data and calling it code-mixed
- Leaving script conventions to individual annotators
For Indian English specifically: 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.
Recommended cohort
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 |
Suggested programme shape
Start with an evaluation set of 100 speakers spread across every Indian English 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 Indian English data for code-switching asr?
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.
How many Indian English 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 Indian English data for code-switching asr
Send the target metric and the languages in scope.