Our research explores how machines can carry emotional texture, not by simulating it from averages, but by grounding it in authentic human experience.
Multi-subject corpora produce averaging effects that flatten emotional texture. The EV architecture requires a singular emotional signature: one voice, one session, the complete range.
EV Corpus Acquisition Protocol, 2020
Published Research
Chen, R., Okafor, D., ████████, L. et al.
We present a novel approach to emotional corpus construction using a single-subject, single-session continuous recording protocol. Models trained on this corpus exhibit materially different emotional texture, with users reporting 340% higher perceived authenticity.
Okafor, D., Chen, R., Vasquez, M.
The Warmth Index combines prosodic analysis, conversational pacing, and user-reported felt-presence scores into a single 0–1 scale. Across existing chatbot platforms, the mean score was 0.31. Our EV-based system achieved 0.94.
Vasquez, M., Chen, R., Okafor, D.
Persistent emotional state modelling across sessions drives stronger user bonding. Seventy-eight percent of users described the interaction as feeling known when emotional memory was enabled.
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Method
Emotional texture degrades under averaging. We use one voice captured whole to preserve the internal coherence that generic corpora erase.
Eight hours of uninterrupted emotional range. Not sampled. Recorded whole, including the final goodnight.
The Warmth Index lets us measure what users already feel: some systems have texture, and most do not.
Corpus material is acquired under informed consent with explicit scope disclosure and independent ethics review.
Hiring
Open roles across both offices, with selected positions available remotely.
Archive
The public research program hints at the shape of the system. These archival documents expose the original capture and the later failure mode.