The effect of speech pathology on automatic speaker verification: a large-scale study.

ABSTRACT: Navigating the challenges of data-driven speech processing, one of the primary hurdles is accessing reliable pathological speech data. While public datasets appear to offer solutions, they come with inherent risks of potential unintended exposure of patient health information via re-identification attacks. Using a comprehensive real-world pathological speech corpus, with over n[Formula: see text]3800 test subjects spanning various age groups and speech disorders, we employed a deep-learning-driven automatic speaker verification (ASV) approach. This resulted in a notable mean equal error rate (EER) of [Formula: see text], outstripping traditional benchmarks. Our comprehensive assessments demonstrate that pathological speech overall faces heightened privacy breach risks compared to healthy s

SUBMITTER: Tayebi Arasteh S 

PROVIDER: S-EPMC10665418 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature