Speech Emotion Recognition · অসমীয়া
Assamese Data for Speech Emotion Recognition
Detecting frustration, satisfaction and escalation in Indian-language customer conversations. In Assamese, the binding constraint is usually dialect coverage and code-mixing, not raw hours.
- Language
- Assamese
- Primary metric
- Per-class F1
- Typical volume
- 100-500 hours
Data profile required
- Elicited and natural emotional speech
- Multi-rater emotion labels with adjudication
- Balanced across emotion classes
What Assamese adds to the requirement
- Assamese has the voiceless velar fricative /x/, unique among major Indian languages and routinely mis-modelled
- No retroflex-dental contrast in the way Hindi has it, so Hindi-derived phone sets over-generate
- Dialects to cover: Kamrupi, Goalparia, Upper Assam (Sibsagar standard), Barak Valley contact varieties
- Assamese speech mixes Hindi, English and Bengali, with substantial contact influence in Barak Valley and tea-garden communities.
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 Assamese specifically: Extremely low-resource. Almost no spontaneous Assamese speech data exists publicly, and non-standard dialects have none.
Recommended cohort
Expect longer fielding times and higher per-hour cost than for Hindi or Marathi; the speaker pool with transcription-grade literacy is smaller.
| Dimension | Typical split | Why it matters for Assamese |
|---|---|---|
| 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 Assamese forms that younger urban speakers have lost |
| Region | Assam / Arunachal Pradesh / parts of Nagaland and Meghalaya and others | Dialect spread across 4 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 80 speakers spread across every Assamese dialect in scope, collected before training data. Then field 100-500 hours of training data from disjoint speakers.
This ordering is what makes the improvement measurable rather than assumed.
Frequently asked
Is there usable public Assamese data for speech emotion recognition?
Extremely low-resource. Almost no spontaneous Assamese speech data exists publicly, and non-standard dialects have none.
How many Assamese speakers do we need?
300-800 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 Assamese data for speech emotion recognition
Send the target metric and the languages in scope.