Abstract
With the advancements in deep learning (DL) and an increasing interest in data-driven speech processing methods, a major challenge for speech data scientists in the healthcare domain is the anonymization of pathological speech, which is a required step to be able to make them accessible as a public training resource. In this paper, we investigate pathological speech data and compare their speaker verifiability with that of healthy individuals. We utilize a large pathological speech corpus of more than 2,000 test subjects with various speech and voice disorders from different ages and apply DL-based automatic speaker verification (ASV) techniques. As a result, we obtained a mean equal error rate (EER) of 0.86% with a standard deviation of 0.16%, which is a factor of three lower than comparable healthy speech databases. We further perform detailed analyses of external influencing factors on ASV such as age, pathology, recording environment, and utterance length, to explore their respective effect. Our findings indicate that speech pathology is a potential biomarker in ASV. This is potentially of high interest for the anonymization of pathological speech data.
Abstract (translated)
URL
https://arxiv.org/abs/2204.06450
