LLM human data · Speech Emotion Recognition
Human Data for LLM Projects for Speech Emotion Recognition
Human-generated text and speech for LLM training and evaluation in Indian languages: prompts, preference rankings, instruction-response pairs, red-teaming and cultural-fit review. Applied to speech emotion recognition, the specification is driven by one thing: per-class f1.
- Service
- LLM human data
- Use case
- Speech Emotion Recognition
- Primary metric
- Per-class F1
Required data profile
- Elicited and natural emotional speech
- Multi-rater emotion labels with adjudication
- Balanced across emotion classes
Technical specification
| Parameter | Standard |
|---|---|
| Task types | Prompt writing, response ranking, instruction-response pairs, adversarial testing |
| Languages | Any language in the network, including code-mixed Hinglish |
| Contributors | Screened by domain, education band and language proficiency |
| Agreement | Overlapping assignments with adjudication |
| Provenance | Per-item contributor and time records |
Process
- Task specification and rubric design
- Contributor screening against the rubric
- Calibration round with feedback
- Production with overlap and gold items
- Adjudication and delivery
Metrics this feeds
- Per-class F1
- Inter-rater agreement on labels
- Escalation detection latency
Failure modes to design out
- Acted emotion only
- Single-rater labels on an inherently subjective task
Every item is traceable to a screened contributor, which matters when a model vendor audits your data provenance.
Deliverables
- Task data in your schema
- Rubric and calibration results
- Contributor metadata (anonymised)
- Agreement statistics
Frequently asked
Is llm human data the right service for speech emotion recognition?
It covers elicited and natural emotional speech. Most speech emotion recognition programmes combine it with at least one other service; we will say so in the scope rather than selling one line item.
What languages are available?
All 14 languages in the network plus Indian English accent bands.
How is the evaluation set handled?
Collected first, from speakers disjoint from the training cohort, so improvement is measurable.
Scope llm human data for speech emotion recognition
Send the metric you need to move.