AI-assisted vocal emotion check-ins without the overclaiming.
FaceofMind uses vocal tone analysis and transcript valence as a clinical tracking support signal, keeping the experience private, consent-based, and non-diagnostic.
Reflective check-in
Vocal Affect Analysis
Signal mix
Vocal tone + transcript
How the check-in feels
Acoustic evaluation runs asynchronously at session end, preventing live talk latency.
Optional voice check-in
The user chooses when to start a voice-based wellness session. Your microphone is only active with your explicit permission.
AI-assisted analysis
Voice prosody (pitch variations, speech speed, tremors) is evaluated together with transcript content to calculate emotional alignment.
Pattern summary
FaceofMind turns vocal trends into visual, readable indicators, completely bypassing medical labels.
Measure vocal affect alongside daily mood surveys.
Strong wellness UX does not rely on a single input. FaceofMind combines self-reported daily moods with deep voice tone analysis (speed, volume consistency, pitch variation) to present a robust, combined timeline.
Supported emotion labels
Core setEmotion labels are reflective wellness signals. They should be confirmed by the user and never presented as clinical diagnosis.
Privacy and trust stay visible.
Vocal tone analysis touches sensitive data, so the page needs to say what matters: user control, privacy, and responsible limits.
Position this feature as an optional wellness aid. For crisis care, emergency support, and clinical judgment, users should be directed to qualified services and professionals.
Reflect on emotions with more context.
Try FaceofMind for mood tracking, AI support, wellness games, and privacy-conscious check-ins.