Conversational AI Companies · Speech Emotion Recognition
Speech Emotion Recognition Data for Conversational AI Companies
Voice-bot and chat-plus-voice platforms deploying into Indian markets, where the gap between demo accuracy and live accuracy is a code-mixing problem. Detecting frustration, satisfaction and escalation in Indian-language customer conversations.
- Buyer
- Conversational AI Companies
- Use case
- Speech Emotion Recognition
- Metric
- Per-class F1
Where the two meet
Bots trained on clean single-language data fail on real switching mid-utterance That is a speech emotion recognition problem, and it is solved by data shaped like this:
- Elicited and natural emotional speech
- Multi-rater emotion labels with adjudication
- Balanced across emotion classes
Your evaluation criteria
- Does the data include overlap, interruptions and backchannels?
- Are utterances collected over the same channel conditions as production?
- Is intent labelling done against your live taxonomy?
Metrics
- Per-class F1
- Inter-rater agreement on labels
- Escalation detection latency
Pitfalls
- Acted emotion only
- Single-rater labels on an inherently subjective task
Contract points
- Scenario confidentiality
- Right to reuse across bot versions
- Delivery in a format that drops into an existing pipeline
Frequently asked
What does a first engagement look like?
Usually a scoped pilot: one language, an evaluation set plus a first training batch, delivered in three to five weeks, followed by the full programme.
Can you match our existing vendor's schema?
Yes. Working to your schema avoids a conversion pass and keeps deliveries comparable across vendors.
How is provenance documented?
Per-item contributor records and consent mapped to IDs in the manifest.
Send your requirement
Language, volume, metric, deadline.