Speech Emotion Recognition · ಕನ್ನಡ
Kannada Data for Speech Emotion Recognition
Detecting frustration, satisfaction and escalation in Indian-language customer conversations. In Kannada, the binding constraint is usually dialect coverage and code-mixing, not raw hours.
- Language
- Kannada
- Primary metric
- Per-class F1
- Typical volume
- 250-1,000 hours
Data profile required
- Elicited and natural emotional speech
- Multi-rater emotion labels with adjudication
- Balanced across emotion classes
What Kannada adds to the requirement
- North Karnataka speech has markedly different intonation and lexicon from Mysuru standard
- Retroflex ಳ and the archaic ಱ appear in older speakers and place names
- Dialects to cover: Bangalore urban, Mysuru (standard literary), Dharwad / North Karnataka, Mangaluru coastal
- Bengaluru is a migration city: Kannada speech there is mixed with English, Hindi, Tamil and Telugu. Native-only Kannada cohorts recruited in Bengaluru are hard to fill without screening for years of residence.
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 Kannada specifically: Mysuru/Bengaluru standard dominates. North Karnataka (Dharwad, Kalaburagi) and coastal Mangaluru speech are barely represented in any public corpus.
Recommended cohort
Screen Bengaluru participants for native fluency and years of Karnataka residence; otherwise the cohort drifts towards second-language Kannada.
| Dimension | Typical split | Why it matters for Kannada |
|---|---|---|
| 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 Kannada forms that younger urban speakers have lost |
| Region | Karnataka / parts of Maharashtra, Tamil Nadu and Andhra Pradesh 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 Kannada dialect in scope, collected before training data. Then field 250-1,000 hours of training data from disjoint speakers.
This ordering is what makes the improvement measurable rather than assumed.
Frequently asked
Is there usable public Kannada data for speech emotion recognition?
Mysuru/Bengaluru standard dominates. North Karnataka (Dharwad, Kalaburagi) and coastal Mangaluru speech are barely represented in any public corpus.
How many Kannada speakers do we need?
500-1,500 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 Kannada data for speech emotion recognition
Send the target metric and the languages in scope.