Clustering Aphasic Speech: A Comparative Study of Feature Extraction Techniques for Fluent and Non-Fluent Categories.

Aphasia assessment plays a crucial role in rehabilitating people with aphasia (PWA), including classifying healthy individuals, identifying subtypes of aphasia, and assessing severity. This study explores unsupervised clustering methods in aphasic speech data, comparing five feature extraction approaches and four clustering algorithms. Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), Agglomerative clustering, K-means, and Gaussian Mixture Models. The focus is on distinguishing between fluent and non-fluent aphasia and subtypes such as Broca's, Wernicke's, and anomic aphasia. The feature extraction methods include Mel-Frequency Cepstral Coefficients (MFCCs), OpenL3 embeddings, their combination, and a fusion of MFCC with chroma features, spectral contrast, zero-crossing rate, and a novel deep convolutional autoencoder-based (CAE) model. These methods are evaluated for their ability to capture key characteristics of aphasic speech, with the clustering results assessed using intrinsic evaluation metrics. Our findings highlight the effectiveness of different feature extraction methods, with CAE emerging as the most effective approach in distinguishing aphasic speech categories and improving clustering performance. This research highlights the potential of unsupervised learning in aphasia research and offers insight into automated diagnosis and speech therapy tools, contributing to enhanced aphasia assessment and rehabilitation.