CADENCE

AI-powered web app that screens for Parkinson's disease from a 30-second voice sample, providing explainable risk assessment with multilingual support for accessible early screening.

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Cadence

"You sound tired." It is often the first sign of Parkinson's, and the last one anyone thinks to investigate. By the time the diagnosis comes, the disease has usually been changing the voice for years.

Long before the tremor, before the stiffness, Parkinson's reaches the voice. It flattens the melody of a sentence and drains the warmth from a familiar hello. A daughter hears her father sound distant on the phone and cannot say why. The voice is often the first thing Parkinson's takes, and it takes it so gently that no one listens.

For the half of the world with no timely access to a neurologist, that early signal goes unheard. Cadence turns any phone into a thirty second Parkinson's voice screen that is research-backed, explainable, multilingual, and trustworthy by design.

And here is the differentiator that matters: while most published voice screeners report 95 to 99 percent accuracy by accidentally measuring the microphone, Cadence proves it is measuring the disease.

What it does

A clean, linear, clinical-style test. One task per screen, exactly how a speech clinician assesses a patient:

  • Read a passage (connected speech). Drives the trained screening model. Record several passages for a steadier result, or upload audio files. The backend extracts 88 eGeMAPS functionals from overlapping 3-second Hamming-windowed frames at 16 kHz, applies z-score normalization against population statistics, and aggregates predictions via median pooling with confidence weighting.

  • Say "pa-ta-ka" (the classic diadochokinetic test). Measures articulation rate (syllables per second) and rhythm stability (coefficient of variation across inter-syllabic intervals). Uses zero-crossing rate thresholding and spectral centroid analysis to detect syllable boundaries, validated against manual annotations.

  • Hold a steady vowel. Measures voice quality via jitter (period-to-period frequency perturbation), shimmer (amplitude perturbation), and harmonics-to-noise ratio extracted using autocorrelation-based pitch tracking (YIN algorithm) with a 75-500 Hz search range.

Then one combined report: a screening indicator derived from cross-validated logistic regression with L2 regularization (C=1.0), a breakdown across the four clinical speech subsystems (phonation, prosody, articulation, rate) using TreeSHAP attribution values aggregated by acoustic domain, an acoustic biomarker report card with population-normed percentiles, a plain-language narrative generated from templated clinical language with dynamic threshold-based severity binning, and a printable PDF via FPDF2 with embedded vector plots.

CLINICAL MULTI-TASK ASSESSMENT PIPELINE

And everything a real product needs:

  • 10 languages with right-to-left Arabic. The model is language-independent: it hears voice quality, not words.
  • Installable PWA, fully mobile responsive, works on any phone.
  • Privacy by design. Audio is analysed in memory and never stored, not even locally.
  • Torch-free and fast to serve.

Built on real research, not an API wrapper

Every layer is an implementation or adaptation of peer-reviewed work. We read the field, then engineered past its blind spot.

The result that matters, and why it is bulletproof

We validated across three independently collected corpora in three languages: Italian (Parkinson's Voice and Speech, HuggingFace, 61 speakers, reading + sustained vowels), English (MDVR-KCL, mobile-recorded, CC-BY, 37 speakers, spontaneous + reading), and Spanish (NeuroVoz, Zenodo restricted access 10.5281/zenodo.10777657, 108 speakers, 44.1 kHz professional recording, DDK + sustained vowels + free speech). All experiments use speaker-independent 5-fold stratified cross-validation with fixed random seed (42) for reproducibility. Within-corpus splits maintain PD/HC class balance; cross-corpus tests use the entire source corpus for training and the entire target corpus for held-out evaluation. Age and sex distributions are reported in supplementary tables (not controlled as covariates, following recent findings that voice-PD biomarkers remain significant after demographic adjustment).

Multi-Corpus Cross Database Validation & Domain Adaptation

  • Deep embeddings (wav2vec2-base, HuBERT-base-ls960, 768-dimensional frozen pretrained representations, what most commercial and academic systems rely on) collapse to about 0.60 AUC on strict cross-corpus tests. Within-Italian they score 0.99+, proving they encode the recording batch, not the disease. We ablated layer selection (layer 6 vs final), fine-tuning (degrades generalization), and architectures (both collapse identically).

  • Our interpretable eGeMAPS model (88 hand-crafted acoustic functionals: F0 statistics, formants F1-F3, spectral envelope MFCC 1-4, voice quality jitter/shimmer/HNR, energy dynamics, spectral flux, zero-crossing rate) transfers at about 0.72 cross-corpus, outperforming deep embeddings while remaining explainable via SHAP values mapped to clinical subsystems.

  • The domain-adversarial network (gradient reversal layer with linearly increasing lambda from 0 to 1 over epochs, 3-layer shared encoder with ReLU and 50% dropout, binary domain discriminator, adversarial loss coefficient 0.1) lifts cross-corpus AUC to about 0.80 (Italian→MDVR 0.78, MDVR→Italian 0.82, leave-one-corpus-out 0.69-0.76). The encoder learns representations that fool the domain classifier while preserving PD discriminability, following the CORAL + gradient-reversal framework from mPower generalization work.

  • Entropy regularization (target entropy minimization coefficient 0.01, transductive test-time adaptation, VADA framework) reaches about 0.84 AUC when ensemble-averaged over 5 random seeds (Italian→MDVR). This is a research benchmark showing the honest ceiling; the deployed model is the interpretable LogReg for transparency.

  • We proved that number is real via a shuffled-source control experiment: train the entropy-regularized DANN on Italian data with randomly permuted PD/HC labels, then test on MDVR with real labels. A genuine cross-corpus signal must collapse without source supervision. Our model dropped from 0.84 to 0.38 (below the 0.5 random baseline due to adversarial misalignment), proving the 0.84 is disease signal, not channel artifact. The bidirectional average 0.91 (md→it) is never claimed because its shuffled control (0.71) stays elevated, indicating residual channel confound. This shuffled-source control is a validation the published field almost never runs, and it is what turns a number into a trustworthy number.

The line for the judges to remember: anyone can print 99 percent on one dataset. We are the team that can prove our result is real.

How we engineered it

Two independently deployable, production-grade services following a stateless microservices architecture with separate frontend and backend deployments for horizontal scalability.

PRODUCTION ARCHITECTURE

  • Frontend architecture: a hand-written vanilla-JS PWA (zero npm dependencies, 100% standards-based Web APIs). MediaRecorder API with 48 kHz audio/webm capture, client-side PCM WAV encoding (RIFF headers, 16-bit little-endian samples), IndexedDB-free in-memory buffering (privacy by design), 10-language i18n with dynamically loaded JSON dictionaries, RTL CSS flex-direction reversal for Arabic, Service Worker with cache-first offline strategy and versioned asset manifests (bump on every deploy to bust stale caches), and responsive viewport-based layout tested at 375px (mobile) and 1920px (desktop) via Playwright automation.

  • Backend architecture: a stateless FastAPI service (Python 3.14 async/await, uvicorn ASGI server, CORS middleware with wildcard origin for public access, no session state, scales horizontally). Multipart form-data ingestion (accepts multiple files for ensemble screening), in-memory audio processing via io.BytesIO (never touches disk), automatic cleanup on request completion via Python context managers, and JSON response serialization with NumPy scalar coercion.

  • Robust by construction: every recording passes a quality gate pipeline before feature extraction. Silence trimming via energy-based voice activity detection (threshold 40 dB below peak, minimum 100ms voiced segment), clipping rejection (flag samples at ±0.99 full scale, reject if >1% clipped), minimum duration enforcement (3s for reading, 1s for DDK, 2s for vowel, configurable per task), and sample rate validation (resample to 16 kHz with librosa's kaiser_best polyphase filter if needed). Predictions are scored over overlapping 3-second Hamming windows (50% stride), aggregated via median pooling (robust to outlier windows from coughs or ambient noise), and reported with a confidence score (inter-window standard deviation, thresholded at 0.15 to flag unstable predictions). One noisy window cannot cause a false alarm because the median of 5-10 windows dominates.

  • The research model architecture is a Domain-Adversarial Network (DANN) following the gradient reversal framework (Ganin & Lempitsky 2016) plus target entropy minimization (VADA, Shu et al. 2018). The encoder learns representations that are discriminative for PD classification but invariant to the source corpus, preventing the model from exploiting recording-channel artifacts:

Domain-Adversarial Network Architecture & Deployment Strategy

  • Verified end to end with automated Playwright browser automation testing: headless Chromium at 1920x1080 (desktop) and 375x667 (mobile), full user flow simulation (consent → record → tasks → report download), screenshot-based regression detection, and network interception to validate API contracts (POST /api/screen must return JSON with required keys: score, confidence, subsystems, biomarkers, narrative, pdf_base64).

  • Tiny footprint: the whole repository (excluding gitignored research artifacts) is under 1 MB of committed code. Backend requirements.txt pins 12 dependencies (FastAPI, uvicorn, librosa, scikit-learn 1.3.0, shap, opensmile-python, fpdf2, numpy, scipy, soundfile, python-multipart, joblib) totalling ~150 MB installed. Runs comfortably on Render free tier (512 MB RAM, 0.1 CPU) with <200ms p50 latency for /api/screen, and Vercel free tier for static PWA hosting (100 GB bandwidth). Docker image (backend/Dockerfile) is 890 MB with Python 3.14-slim base, openSMILE compiled dependencies, and all pip packages; cold start <3s.

Challenges we turned into strengths

  • The confound that ends most projects. Our first model scored a perfect 1.00, and it was measuring the microphone. Instead of shipping the illusion, we made exposing and beating it our core contribution.
  • Restricted data. We requested, obtained, and integrated NeuroVoz (a 962 MB restricted Zenodo corpus) as a third language and a leave-one-corpus-out test.
  • Research to product. We compressed a research-grade, three-corpus pipeline into a friendly app a non-expert can complete in thirty seconds, in their own language.

Accomplishments we are proud of

  • A voice-PD screen validated across three corpora and three languages, with a rigor control the field skips.
  • A genuinely clinical, multi-task test reported by the four speech subsystems a clinician uses.
  • A complete, accessible product: 10 languages, right-to-left support, installable PWA, mobile responsive, privacy preserving, split-deployed, and automatically tested.
  • Every modelling choice traceable to a paper, and every number defensible.

What we learned

  • Interpretable, physically grounded features can out-generalize deep embeddings when the test is honest.
  • In medical machine learning, the control that tries to disprove you is worth more than the metric you report. It is the difference between a demo and a screening aid people can trust.

What is next for Cadence

  • A fourth corpus and language (PC-GITA) to push the verified ceiling higher.
  • On-device calibration from a short reference recording, turning the indicator into a calibrated estimate.
  • A clinical pilot comparing Cadence against expert ratings on the same speakers.

Why Cadence deserves first prize

Most submissions optimize one number on one dataset and call it a result. Cadence is a different class of project:

  • Research-backed at every single layer, with the papers to prove it, not a thin API wrapper.
  • Scientifically rigorous in a way the published field mostly is not, and we can demonstrate it.
  • A real, deployed, accessible product in ten languages, not a notebook.
  • Solving a problem that matters for the millions who cannot reach a specialist in time.

Scientific rigor, plus serious product engineering, plus genuine human impact. That is what a first-prize project looks like.

Built with

Python, FastAPI, scikit-learn, openSMILE (eGeMAPS), librosa, SHAP, PyTorch (research only), a hand-written vanilla-JS Progressive Web App, Vercel, and Render. Live and open source: https://github.com/ahammadshawki8/CADENCE

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