A 30-second counting task, embedded with speaker-identification models, predicts male sleep apnea (AUC 0.64) and shows gender- and condition-specific model rankings across a new 7,188-recording clinical speech benchmark.
Survey on biomarkers in human vocalizations
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abstract
Recent years has witnessed an increase in technologies that use speech for the sensing of the health of the talker. This survey paper proposes a general taxonomy of the technologies and a broad overview of current progress and challenges. Vocal biomarkers are often secondary measures that are approximating a signal of another sensor or identifying an underlying mental, cognitive, or physiological state. Their measurement involve disturbances and uncertainties that may be considered as noise sources and the biomarkers are coarsely qualified in terms of the various sources of noise involved in their determination. While in some proposed biomarkers the error levels seem high, there are vocal biomarkers where the errors are expected to be low and thus are more likely to qualify as candidates for adoption in healthcare applications.
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HPP-Voice: A Large-Scale Evaluation of Speech Embeddings for Multi-Phenotypic Classification
A 30-second counting task, embedded with speaker-identification models, predicts male sleep apnea (AUC 0.64) and shows gender- and condition-specific model rankings across a new 7,188-recording clinical speech benchmark.