Behind Hume’s Expression Measurement Models for Face and Voice

Hume AI describes the science, training data, and evaluation methods behind its Expression API, which provides real-time measurements of vocal and facial expression for applications such as data annotation, AI evaluation, and interaction analysis. Its speech emotion model assigns scores across 414 emotional tags, while its voice descriptor model measures 190 vocal characteristics, and its facial model recognizes 48 emotional categories plus 27 visible facial features. The models were trained using human-rated resources including more than 370,000 facial images with over one million ratings and more than 260,000 speech recordings. In evaluations against human judgments and public benchmarks, Hume reports that its speech emotion model achieved the highest overall emotion-ranking AUC of 77.1 among 11 compared systems across over 9,000 recordings from 32 datasets, ahead of Gemini 3.8 Flash at 73.0, though its performance was similar to Gemini on an internal evaluation of finer-grained emotional expressions across 16 languages.

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