Leveraging AM and FM Rhythm Spectrograms for Dementia Classification and Assessment
Parismita Gogoi, Vishwanath Pratap Singh, Seema Khadirnaikar, Soma Siddhartha, Sishir Kalita, Jagabandhu Mishra, Md Sahidullah, Priyankoo Sarmah, S. R. M. Prasanna
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/seemark11/dhinirnayaamfmOfficialIn papernone★ 2
Abstract
This study explores the potential of Rhythm Formant Analysis (RFA) to capture long-term temporal modulations in dementia speech. Specifically, we introduce RFA-derived rhythm spectrograms as novel features for dementia classification and regression tasks. We propose two methodologies: (1) handcrafted features derived from rhythm spectrograms, and (2) a data-driven fusion approach, integrating proposed RFA-derived rhythm spectrograms with vision transformer (ViT) for acoustic representations along with BERT-based linguistic embeddings. We compare these with existing features. Notably, our handcrafted features outperform eGeMAPs with a relative improvement of 14.2\% in classification accuracy and comparable performance in the regression task. The fusion approach also shows improvement, with RFA spectrograms surpassing Mel spectrograms in classification by around a relative improvement of 13.1\% and a comparable regression score with the baselines.