Emotion inference is the automated estimation of a person’s mood or emotional state from signals such as facial movement, gaze, speech patterns, and voice tone. It is a form of affective computing: the system does not just recognize words or identities, it tries to infer internal state from behavior. In security terms, that makes it highly sensitive because the result is a derived profile, not a simple raw input.
This matters because inferred emotion can influence moderation, risk scoring, age checks, and access decisions. In real products, it may run continuously during a call or recording session, creating privacy and retention concerns even when the user never types anything sensitive. Attackers can also try to evade or distort these models with masking, synthetic media, or adversarial changes to expression and speech. Defenders need clear consent, tight retention limits, and careful validation, because false positives can trigger unnecessary review or blocking.