Leveraging AM and FM Rhythm Spectrograms for Dementia Classification and Assessment
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916793750126592 |
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| author | Gogoi, Parismita Singh, Vishwanath Pratap Khadirnaikar, Seema Siddhartha, Soma Kalita, Sishir Mishra, Jagabandhu Sahidullah, Md Sarmah, Priyankoo Prasanna, S. R. M. |
| author_facet | Gogoi, Parismita Singh, Vishwanath Pratap Khadirnaikar, Seema Siddhartha, Soma Kalita, Sishir Mishra, Jagabandhu Sahidullah, Md Sarmah, Priyankoo Prasanna, S. R. M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00861 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging AM and FM Rhythm Spectrograms for Dementia Classification and Assessment Gogoi, Parismita Singh, Vishwanath Pratap Khadirnaikar, Seema Siddhartha, Soma Kalita, Sishir Mishra, Jagabandhu Sahidullah, Md Sarmah, Priyankoo Prasanna, S. R. M. Audio and Speech Processing Sound 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. |
| title | Leveraging AM and FM Rhythm Spectrograms for Dementia Classification and Assessment |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2506.00861 |