# Multiview Canonical Correlation Analysis for Automatic Pathological Speech Detection

arXiv: https://arxiv.org/abs/2409.17276 · HTML: https://mlsanity.com/paper/2409.17276/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [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)
- [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)
- [Graph Multiview Canonical Correlation Analysis](https://mlsanity.com/paper/1811.12345/) (2018)
- [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)
- [A Comparison of Relaxations of Multiset Cannonical Correlation Analysis and Applications](https://mlsanity.com/paper/1302.0974/) (2013)
- [Impact of Speech Mode in Automatic Pathological Speech Detection](https://mlsanity.com/paper/2406.09968/) (2024)

## Built on by (most cited first, 1 of 1)

- [Chirp Localization via Fine-Tuned Transformer Model: A Proof-of-Concept Study](https://mlsanity.com/paper/2503.22713/) (2025)

## Abstract (arXiv)

Recently proposed automatic pathological speech detection approaches rely on spectrogram input representations or wav2vec2 embeddings. These representations may contain pathology irrelevant uncorrelated information, such as changing phonetic content or variations in speaking style across time, which can adversely affect classification performance. To address this issue, we propose to use Multiview Canonical Correlation Analysis (MCCA) on these input representations prior to automatic pathological speech detection. Our results demonstrate that unlike other dimensionality reduction techniques, the use of MCCA leads to a considerable improvement in pathological speech detection performance by eliminating uncorrelated information present in the input representations. Employing MCCA with traditional classifiers yields a comparable or higher performance than using sophisticated architectures, while preserving the representation structure and providing interpretability.