Detection of Type 2 Diabetes from 20-second Speech Recordings: A Large-Scale Validation Study

Abstract Accessible screening for type 2 diabetes (T2D) is critical, with millions of cases remaining undiagnosed globally. Here, we present the largest known real-world validation study for a speech-based T2D prediction model, trained on speech data from over 21,000 individuals, that works on features extracted from 20-second speech recordings. The model was evaluated in two stages: 1) Against self-reported diagnoses in 7,319 English-speaking participants using AUC, and 2) Against HbA1c blood tests in a subset of 801 participants drawn from the full cohort. Performance was also compared against QDiabetes and in the presence of key confounding variables. The model demonstrated clinically useful predictive capacity on self-reported data (AUC = 0.80 ± 0.03), approaching QDiabetes (AUC = 0.86 ± 0.03). It was robust to most demographic confounds (e.g., age and sex) and medication use, with reduced performance in the presence of comorbidities (e.g., cardiovascular disease and hypertension). At diabetes threshold of HbA1c ≥48 mmol/mol, the model achieved an AUC of 0.75 (±0.07). This biomarker-validated speech-based tool demonstrates potential to complement existing methods through accessible, scalable screening requiring only a 20-second speech sample.