Selfsupervised learning for pathological speech detection

TL;DR

This paper explores the use of self-supervised learning embeddings, like wav2vec2, to improve automatic detection of pathological speech disorders, addressing data scarcity and aiming for more accurate, efficient diagnosis.

Contribution

It introduces a novel application of self-supervised speech representations for pathological speech detection, enhancing performance with limited labeled data.

Findings

Self-supervised embeddings improve detection accuracy.

Multilingual models show robustness across languages.

Enhanced representations reduce reliance on large labeled datasets.

Abstract

Speech production is a complex phenomenon, wherein the brain orchestrates a sequence of processes involving thought processing, motor planning, and the execution of articulatory movements. However, this intricate execution of various processes is susceptible to influence and disruption by various neurodegenerative pathological speech disorders, such as Parkinsons' disease, resulting in dysarthria, apraxia, and other conditions. These disorders lead to pathological speech characterized by abnormal speech patterns and imprecise articulation. Diagnosing these speech disorders in clinical settings typically involves auditory perceptual tests, which are time-consuming, and the diagnosis can vary among clinicians based on their experiences, biases, and cognitive load during the diagnosis. Additionally, unlike neurotypical speakers, patients with speech pathologies or impairments are unable to access various virtual assistants such as Alexa, Siri, etc. To address these challenges, several automatic pathological speech detection (PSD) approaches have been proposed. These approaches aim to provide efficient and accurate detection of speech disorders, thereby facilitating timely intervention and support for individuals affected by these conditions. These approaches mainly vary in two aspects: the input representations utilized and the classifiers employed. Due to the limited availability of data, the performance of detection remains subpar. Self-supervised learning (SSL) embeddings, such as wav2vec2, and their multilingual versions, are being explored as a promising avenue to improve performance. These embeddings leverage self-supervised learning techniques to extract rich representations from audio data, thereby offering a potential solution to address the limitations posed by the scarcity of labeled data.