Multimodal emotion recognition · PHQ-9
Reading well-being from a face, a voice, and a questionnaire.
A research demo that pairs deep-learning emotion recognition with the clinically validated PHQ-9 — all running in your browser, nothing stored.
Facial emotion
Your webcam streams to a convolutional network trained on FER2013, classifying expression frame by frame.
Speech emotion
Record a few seconds; a 1-D CNN trained on RAVDESS predicts a combined gender and emotion class from your voice.
PHQ-9 screening
The standard nine-item depression-screening questionnaire, scored with severity bands and guidance.
What this is
A demo of multimodal well-being screening
What it's for
A hands-on demonstration of how machine learning can read emotional cues, paired with a validated questionnaire. Useful for:
- Quick self-reflection & mood check-ins — gauge how you're presenting right now.
- Showcasing an end-to-end ML pipeline — vision + audio models behind a web app.
- Education & awareness — see what affect-recognition can (and can't) do.
How to use it
- Pick a modalityFace (webcam), Voice (mic), or the PHQ-9 form.
- Run it in your browserGrant camera/mic access — nothing is uploaded or stored.
- Read the resultAn emotion cue, or a scored PHQ-9 severity band with guidance.
Emotion recognition is a supplementary signal, not a diagnosis. The PHQ-9 is the only clinically validated component, and it is a screening aid — not a substitute for a clinician.