Technical Insight
50 Recognized Emotions vs 17 Expressed Emotions: Evaluating AI Modules
LANCUN EmoMonte recognizes 50 emotions and expresses 17, with an evaluation framework for quality, policy, robustness and safety.
Direct answer
LANCUN EmoMonte recognizes 50 emotions and autonomously expresses 17. A complete evaluation also asks whether the system recognizes meaningful signals in the target environment, expresses an appropriate voice and action, changes dialogue policy usefully, and falls back safely when uncertain or wrong.
Key points
- LANCUN EmoMonte supports 50 recognized emotions and 17 expressed emotions.
- Measure recognition, expression and policy effects separately.
- Include noise, age, accents, irony and ambiguous expression in the test set.
- Use conservative behavior in high-risk contexts and let users correct or disable it.
Label count is not the outcome
Systems merge and split similar states differently, so raw label counts are not directly comparable. For a companion toy, reliably distinguishing calm, excitement, frustration and a request for help may matter more than a long taxonomy.
Map each relevant state to product behavior such as pacing, clarification, reassurance, guardian escalation or action stop before designing the test.
Separate recognition, expression and policy
Recognition includes accuracy, confidence calibration and environmental stability. Expression covers whether speech, text, light and movement are coherent. Policy measures whether context changes the response appropriately.
Also record when the system declines to infer. A neutral response that asks for clarification can be more reliable than a confident but unsupported label.
Use representative tests
Include target ages, accents, speaking rates, distance, noise, overlapping speech, irony and mixed affect. Avoid a benchmark made only from standardized sentences close to training data.
Production analysis should follow appropriate consent and privacy processing, and raw voice or sensitive affect records should not be retained by default.
Design for incorrect inference
Affect inference is probabilistic and should not independently drive medical, psychological, spending or personal-safety decisions. Child and care products need conservative responses, identity notices, human escalation paths and auditability.
Any published label count should include the product definition, model version and test evidence rather than serving as a standalone capability claim.