Speech biomarkers
for clinical trials

A research-grade speech analysis framework for pharmaceutical and clinical research partners. Interpretable acoustic-linguistic features combined with deep learning — explainable digital measures built with regulatory-facing interpretability in mind.

Why voice for clinical research

A short speech sample carries acoustic, linguistic, and prosodic information that maps directly onto the cognitive and motor domains pharma trials are designed to measure.

Low patient burden

Short structured tasks completed remotely

Minimal clinician time for speech acquisition. Suitable for elderly populations and decentralized trial designs.

Lower practice-effect risk

Lower practice effect than cognitive composites

Acoustic and linguistic features do not benefit from stimulus re-exposure in the way scored cognitive tests do.

High frequency

Trajectory information between clinic visits

Frequent acquisition is feasible, including weekly or higher-cadence designs when appropriate — enabling longitudinal trajectory analysis that infrequent clinic visits cannot capture.

Dual-pipeline architecture

Combining the interpretability of curated acoustic-linguistic features with the discriminative power of deep learning — without inheriting the failure modes of either approach alone.

Input

Speech recording

Structured task on mobile device. Multilingual.

Pipeline A · Interpretable

Curated acoustic + linguistic features

Prosody · Fluency · Lexical retrieval · Phonation

Feature-level explainability
Pipeline B · Deep Learning

Speech foundation model embeddings + classifier

Complementary discriminative signal.

Cross-pipeline consistency check

Disagreement → confidence flag surfaced with output.

Output

Biomarker output

+ feature-level explainability
+ confidence flag
+ supports clinically interpretable review

Pipeline A uses curated acoustic and linguistic features encoding high-level structure (lexical retrieval, prosodic rhythm, fluency timing) — robust to the acoustic preprocessing applied by mobile devices.

Pipeline B surfaces signal structures hand-engineered features cannot capture by construction — multi-feature interactions, temporal dependencies, latent prosodic patterns — adding complementary discriminative signal from end-to-end deep-learning representations. Outputs are reconciled; cross-pipeline disagreement surfaces as a confidence flag.

Why architecture matters in deployment

In a longitudinal smartphone-based trial, mobile-device noise-cancellation algorithms change with OS updates. Many speech models are vulnerable to silent instability across these changes. Our architecture is designed to reduce this risk.

Robustness evaluation

Drift in classifier sensitivity across six scenarios of mid-study noise-cancellation algorithm change.

Drift across noise-cancellation scenarios  ·  pp
Stable Drifts
Drift > 20 pp confounds the disease signal
Cephalgo
11.3 pp
Pure deep learning A
42.1 pp
Pure deep learning B
44.3 pp
Curated features only
45.5 pp
0 10 20 30 40
Drift = sensitivity spread (max − min) across six deployment scenarios in which the training noise-cancellation algorithm differs from the test algorithm. Lower drift is better. Italian Parkinson's Voice and Speech Dataset, n = 50. Pure deep-learning and pure feature-based baselines drift 42–46 pp — confounded with the disease-progression signal. Cephalgo's dual-pipeline architecture drifts only 11.3 pp. Reporting follows TRIPOD+AI.
This evaluation was accepted as a selected poster for the 9th Annual Digital Biomarkers in Clinical Trials Summit (Roche, Basel, June 2026), a precompetitive consortium of pharma digital biomarker leads.

Where we fit in your program

Speech biomarkers integrate at multiple points in clinical research — both in retrospective study analysis and in active trials.

Population enrichment

Stratify candidates by progression likelihood

Speech biomarkers complement molecular enrichment with a functional progression signal.

Longitudinal monitoring

Capture trajectory changes clinic visits miss

High-frequency, low-burden remote assessment captures trajectory changes that infrequent clinic-based composites can miss.

Exploratory endpoints

Apply to legacy data, or integrate prospectively

Apply our framework to legacy speech data from completed studies, or integrate prospectively into active protocols.

Collaboration model

We are an R&D partner for translational and exploratory work. We do not require new data collection to begin — if your team has speech data from previous studies, we can apply our framework to it.

Retrospective analysis

Of legacy speech data from completed or ongoing trials.

Exploratory endpoint integration

In active protocols, with no impact on primary endpoint design.

Joint validation studies

On partner-curated cohorts, supporting fit-for-purpose evidence generation.

Contact

Let's talk about your program.

Share your therapeutic area and current studies. We'll respond with a tailored proposal.

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