Custom collection and annotation
Emotion-labeled conversational speech for agents that hear frustration
A voice agent that answers correctly while the caller gets angrier is still failing. Turn-level affect labels on natural conversation give an agent something to detect, and give you something to evaluate it against.
Example availability: Tell us the language, interaction type and label categories. The team will check whether a relevant, permissioned example can be shared, then confirm the available options and timing. If needed, we can scope a small pilot.
Collection specification
- Discrete labels
- Anger, disgust, fear, happiness, sadness, surprise, and neutral per turn across both sides of the exchange
- Dimensional labels
- Valence and arousal on the same turns, for models that need a trend rather than a class
- Speech type
- Spontaneous paired conversation, not read prompts, so the affect is occurring rather than performed
- Handoff points
- Turns annotated where a human would escalate, usable as an evaluation target for handoff timing
- Intent
- Dialogue acts or your own taxonomy, tracked separately from affect
- Coverage
- Language, accent, age, device, and noise quotas set per project
- Pricing
- Custom, scoped to your conditions
Define the affect target before labeling
State whether a label describes audible expression, a reviewer's perception, a participant self-report or an escalation decision. Keep these fields separate. For each category, provide examples, counterexamples and an uncertain or unratable option instead of forcing every turn into a confident emotion class.
Agree how many reviewers label the same material, how disagreements are resolved, and which agreement results are delivered. A taxonomy and a set of adjudicated pilot examples make the acceptance test inspectable before production. Collection method, label schema and QA evidence are confirmed for the project.
Evaluation needs the same labels as training
Detecting frustration is only half the requirement. The other half is whether the agent did anything sensible about it, and that cannot be measured without labeled turns to measure against.
Training and evaluation can share the same schema, but their recordings should be separated according to an agreed speaker and session split. For handoff timing, define the acceptable intervention window and score missed and unnecessary handoffs separately from emotion classification.
This offer is not the right fit when
- You need a ready trained emotion classifier rather than labeled data.
- You only want scripted, acted emotional speech.
- You want text sentiment labels with no audio.
Check example availability
- Your details
- Your project
- Verify
Three short steps. The team checks whether a permissioned example matches your use and can be shared, then follows up with the available options.
Would rather talk it through first? Talk through your label taxonomy
Prepare the brief
Bring a specification your vendors can price consistently
Use the free worksheet to define coverage, rights, delivery fields, held-out rules, and acceptance tests before requesting a collection plan.
Buyer documentation
Specimen data card and provenance structure, evaluation brief template, and acceptance and held-out questions.
Related services
Guides for this decision
Want to talk through your taxonomy first? Category sets, dimensional scales, and agreement thresholds are usually a conversation, not a form field. Book a short call to work through the taxonomy and conditions the labels need to hold up under.