Multiview Canonical Correlation Analysis for Automatic Pathological Speech Detection
This work addresses the challenge of improving pathological speech detection for medical or diagnostic applications, but it is incremental as it builds on existing methods with a specific technique.
The paper tackled the problem of irrelevant information in input representations for automatic pathological speech detection by applying Multiview Canonical Correlation Analysis (MCCA), resulting in a considerable improvement in detection performance.
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.