Multimodal Fusion

Face + Voice + Biosignal. Fused, Not Stitched.

A learned fusion model that combines face, voice, and physiological signals into a single calibrated emotional state stream — outperforming any single modality, and any naive late-fusion baseline.

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Learned fusion

not just averaging

3+ modalities

face · voice · biosignal

Calibrated

probabilities, not labels

Why one modality is never enough

Real emotion lives across the signals — fusion is where it actually shows up.

Architecture

Learned Cross-Modal Fusion

A fusion head trained on time-aligned multi-modal data, not a weighted average of independent classifiers. Handles missing modalities gracefully.

›Time-aligned training data
›Robust to missing channels
›Joint embedding output

Output

Calibrated State Stream

Streams calibrated probability distributions, not single labels — so downstream policies can reason about confidence, not just guess.

›Temperature-calibrated outputs
›Configurable smoothing window
›Confidence and entropy exposed

Integration

Drop-in with Single Models

Pairs natively with the single-modality [[emotion-models]] — use the same label space, same input format, same calibration.

›Shared label space across products
›WebSocket + native callback APIs
›Replay-friendly event log

Where it fits

🎯

High-stakes detection

Where a single noisy channel isn't enough.

🤖

Robotics

Embodied agents need redundant emotional sensing.

🩺

Health & wellness

Calmness and stress reads that survive real-world noise.

🎮

Premium NPCs

Characters that read both face and voice.

Fuse the signals. Stop stitching them.

Developer access includes the fusion model, single-modality models, and a unified streaming SDK.

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