# Selfsupervised learning for pathological speech detection

arXiv: https://arxiv.org/abs/2406.02572 · HTML: https://mlsanity.com/paper/2406.02572/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 16 May 2024.

## Builds on (most cited first, 14 of 14)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings](https://mlsanity.com/paper/2104.03502/) (2021)
- [Machine Learning for Stuttering Identification: Review, Challenges and Future Directions](https://mlsanity.com/paper/2107.04057/) (2021)
- [Wav2vec-based Detection and Severity Level Classification of Dysarthria from Speech](https://mlsanity.com/paper/2309.14107/) (2023)
- [On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches](https://mlsanity.com/paper/2211.08833/) (2022)
- [Experimental investigation on STFT phase representations for deep learning-based dysarthric speech detection](https://mlsanity.com/paper/2110.03283/) (2021)
- [Supervised Speech Representation Learning for Parkinson's Disease Classification](https://mlsanity.com/paper/2106.00531/) (2021)
- [Robust Stuttering Detection via Multi-task and Adversarial Learning](https://mlsanity.com/paper/2204.01735/) (2022)
- [Introducing ECAPA-TDNN and Wav2Vec2.0 Embeddings to Stuttering Detection](https://mlsanity.com/paper/2204.01564/) (2022)
- [End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challenge](https://mlsanity.com/paper/2207.10817/) (2022)
- [Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learning](https://mlsanity.com/paper/2302.11343/) (2023)
- [Stuttering Detection Using Speaker Representations and Self-supervised Contextual Embeddings](https://mlsanity.com/paper/2306.00689/) (2023)

## Abstract (arXiv)

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.