Close up of an acoustic testing studio condenser microphone resting on a wooden stand beside audio calibration monitors under soft diffused light.
Close up of an acoustic testing studio condenser microphone resting on a wooden stand beside audio calibration monitors under soft diffused light.
Read Methodology
— Experimental Research Interface

Test Your Voice

This evaluation interface provides an experimental, model-generated estimate of simulated listener bias across institutional hiring contexts. It does not reflect an individual's actual workplace competence, professional qualifications, or personal employability.

• Input Controls

Acoustic Sample Upload

Select an uncompressed speech recording of standard reading passages. Supported formats include WAV or MP3 files under 10MB, sampled at 16kHz or higher.

/ Architecture Workflow

Processing Pipeline

1. Audio Ingestion

2. Feature Extraction

3. Calibrated Output

The frontend validates format integrity and dispatches audio data via secure HTTP POST payload to the designated Python endpoint.

The server-side features.py module processes acoustic parameters, isolating fundamental pitch, formant variance, speech rate, and rhythmic contours.

Extracted coefficients pass to the trained model estimator, returning a normalized 1–5 score alongside acoustic factor weighting to the browser.

+ Deployment Prerequisites

1.0–5.0

Index Range

Technical Integration Notice

Enabling voice analysis requires external backend hosting, verified CORS security headers, strict file verification, and comprehensive regression validation. Model weights and training assets remain strictly isolated on the server.

0 MB

Client Storage