VOINOSIS

Rather than transcribing speech to text, this capability captures raw vocal signals such as speech rate, intensity, and micro-tremors. These physical traits act as direct indicators of underlying health changes that exist beneath conscious control. Clinicians can gain a screening signal that supports confident decision-making during patient evaluations.

Fusing speech data with eye-tracking signals gives this capability a second, independent data stream to check results against. Patients with limited language ability or literacy can still be screened accurately under this approach. Care teams gain cross-verified outputs instead of depending on a single modality, strengthening confidence in each assessment.

This capability quantifies subtle vocal characteristics, including pitch variation, speech rate, and micro-tremors, that conventional speech recognition tools typically overlook. Because these biomarkers exist beneath conscious control, they offer a physical signal that self-reported symptoms cannot fully capture. Care teams use this output to support more objective, data-backed screening conversations.

Speech is processed in layers under this capability, moving from individual phonemes up through phrases to full utterances rather than treating voice as one flat signal. Each clinical task draws on the extraction layer best suited to it, keeping the analysis matched to what is being measured. This layered approach helps care teams trust the precision behind each result.

Once an initial screening is complete, this capability extends tracking into everyday settings through repeated, low-effort check-ins rather than a single clinical snapshot. Personalized training content adjusts to each person's specific acoustic and cognitive profile over time. This ongoing view gives care teams a clearer basis for deciding when a patient may need further follow-up.