Speech Emotion Recognition · Indian English
Indian English Data for Speech Emotion Recognition
Detecting frustration, satisfaction and escalation in Indian-language customer conversations. In Indian English, the binding constraint is usually dialect coverage and code-mixing, not raw hours.
- Language
- Indian English
- Primary metric
- Per-class F1
- Typical volume
- 500-2,000 hours
Data profile required
- Elicited and natural emotional speech
- Multi-rater emotion labels with adjudication
- Balanced across emotion classes
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
- Per-class F1
- Inter-rater agreement on labels
- Escalation detection latency
Failure modes
- Acted emotion only
- Single-rater labels on an inherently subjective task
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 speech emotion recognition?
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 speech emotion recognition
Send the target metric and the languages in scope.