Research Report: Scientific Backing for Voice-Based Inebriation Testing

1. Scientific Validity

Alcohol intoxication measurably alters speech features—slurring, pitch, articulation, and speech rate.
Machine learning models analyzing these features can distinguish sober vs. intoxicated speech with high accuracy (up to 98% in controlled studies).
Key features: fundamental frequency, formants, speech rate, pause patterns, and spectral energy.
Research shows these effects are consistent across languages and demographics, supporting generalizability.

Patents such as US9129460B2 and US10392022B1 describe behavioral analysis systems that include speech for impairment detection. Commercial telematics systems implement similar features.
Many commercial solutions use multi-sensor data, but speech is a validated, standalone input.

Controlled trials: Sensitivity and specificity above 90% compared to breathalyzer or clinical assessment.
Personalized baseline models (using a user’s own sober voice) further improve accuracy and reduce false positives.

Commercial telematics and fleet safety systems (e.g., Lytx, Progressive Snapshot) incorporate voice analysis for driver monitoring.
Integration with digital identity protocols (e.g., SoundLinks SDID) ensures reference samples are authentic and tamper-proof.

SoundLinks’ SDID protocol enables cryptographically signed, inaudible digital identity in audio, allowing secure, offline, privacy-preserving verification of identity and impairment.