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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2406.07542 |
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| _version_ | 1866910684498886656 |
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| author | Ortiz-Perez, David Garcia-Rodriguez, Jose Tomás, David |
| author_facet | Ortiz-Perez, David Garcia-Rodriguez, Jose Tomás, David |
| contents | Cognitive decline is a natural process that occurs as individuals age. Early diagnosis of anomalous decline is crucial for initiating professional treatment that can enhance the quality of life of those affected. To address this issue, we propose a multimodal model capable of predicting Mild Cognitive Impairment and cognitive scores. The TAUKADIAL dataset is used to conduct the evaluation, which comprises audio recordings of clinical interviews. The proposed model demonstrates the ability to transcribe and differentiate between languages used in the interviews. Subsequently, the model extracts audio and text features, combining them into a multimodal architecture to achieve robust and generalized results. Our approach involves in-depth research to implement various features obtained from the proposed modalities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07542 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cognitive Insights Across Languages: Enhancing Multimodal Interview Analysis Ortiz-Perez, David Garcia-Rodriguez, Jose Tomás, David Machine Learning Artificial Intelligence Cognitive decline is a natural process that occurs as individuals age. Early diagnosis of anomalous decline is crucial for initiating professional treatment that can enhance the quality of life of those affected. To address this issue, we propose a multimodal model capable of predicting Mild Cognitive Impairment and cognitive scores. The TAUKADIAL dataset is used to conduct the evaluation, which comprises audio recordings of clinical interviews. The proposed model demonstrates the ability to transcribe and differentiate between languages used in the interviews. Subsequently, the model extracts audio and text features, combining them into a multimodal architecture to achieve robust and generalized results. Our approach involves in-depth research to implement various features obtained from the proposed modalities. |
| title | Cognitive Insights Across Languages: Enhancing Multimodal Interview Analysis |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2406.07542 |