Speech Emotion Recognition · ગુજરાતી
Gujarati Data for Speech Emotion Recognition
Detecting frustration, satisfaction and escalation in Indian-language customer conversations. In Gujarati, the binding constraint is usually dialect coverage and code-mixing, not raw hours.
- Language
- Gujarati
- 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 Gujarati adds to the requirement
- Murmured (breathy-voiced) vowels are phonemic in Gujarati and are absent from most shared Indic acoustic models
- Surti speech has distinctive intonation and vowel quality that mismatches Ahmedabad-trained models
- Dialects to cover: Standard (Amdavadi), Surti, Kathiyawadi, Kachchhi-influenced
- Business and trade vocabulary is heavily English; Gujarati diaspora speech adds further English structure. Specify whether diaspora speakers are in or out of scope.
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 Gujarati specifically: Very little spontaneous Gujarati audio exists publicly; nearly all of it is Ahmedabad read speech.
Recommended cohort
Surat and Rajkot recruitment is essential for dialect coverage; Ahmedabad-only cohorts sound uniform.
| Dimension | Typical split | Why it matters for Gujarati |
|---|---|---|
| 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 Gujarati forms that younger urban speakers have lost |
| Region | Gujarat / Daman & Diu / Dadra & Nagar Haveli 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 Gujarati 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 Gujarati data for speech emotion recognition?
Very little spontaneous Gujarati audio exists publicly; nearly all of it is Ahmedabad read speech.
How many Gujarati 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 Gujarati data for speech emotion recognition
Send the target metric and the languages in scope.