Self-Supervised Speech Representations for Sleep Apnea Severity Prediction

OBJECTIVE: Obstructive sleep apnea (OSA) is a prevalent yet underdiagnosed condition characterized by repetitive upper airway obstruction during sleep. Current gold standard diagnostic standards rely on polysomnography (PSG), which is resource-intensive. Since upper airway characteristics impact both OSA and speech production, speech processing has emerged as a promising alternative for OSA screening. However, prior work has focused primarily on acoustic features. This study aims to develop a speech-based screening and severity estimation pipeline for OSA using self-supervised learning (SSL) and multimodal acoustic features. METHODS: We proposed a novel fusion framework combining SSL-derived speech representations from pre-trained neural networks with traditional acoustic features and time-frequency representations of speech phase and magnitude. Elongated vowels recorded during wakefulness were used to screen for OSA at two apnea-hypopnea index (AHI) thresholds (10 and 30 events/hour) and to estimate AHI. Data were collected across three research sites, comprising participants of varied sex, race, and OSA severity. RESULTS: For OSA screening, the models achieved balanced accuracies of 0.79 (AHI $\geq$10) and 0.74 (AHI $\geq$30) in females, and 0.80 and 0.78 in males, respectively. AHI estimation yielded mean absolute errors of 12.0 events/hour (r = 0.63) in females and 14.7 events/hour (r = 0.52) in males. CONCLUSION: Our results demonstrate the feasibility of using speech, especially vowel phonation during wakefulness, as a biomarker for OSA risk and severity estimation. The approach generalizes well across diverse demographic groups. SIGNIFICANCE: This study presents a significant step toward accessible, low-burden, and cost-effective OSA screening, with broad implications for scalable sleep health assessments.