Summary of Cognivoice: Multimodal and Multilingual Fusion Networks For Mild Cognitive Impairment Assessment From Spontaneous Speech, by Jiali Cheng et al.
CogniVoice: Multimodal and Multilingual Fusion Networks for Mild Cognitive Impairment Assessment from Spontaneous Speech
by Jiali Cheng, Mohamed Elgaar, Nidhi Vakil, Hadi Amiri
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed CogniVoice framework is a novel multilingual and multimodal approach for detecting Mild Cognitive Impairment (MCI) and estimating Mini-Mental State Examination (MMSE) scores using speech data and its textual transcriptions. The key innovation lies in an ensemble multimodal and multilingual network based on the “Product of Experts” architecture, which mitigates reliance on shortcut solutions. This framework outperforms the best-performing baseline model on MCI classification and MMSE regression tasks by 2.8 and 4.1 points in F1 and RMSE respectively, effectively reducing the performance gap across different language groups by 0.7 points in F1. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CogniVoice is a new way to help doctors detect memory problems in people with Mild Cognitive Impairment (MCI). MCI makes it harder for people to remember things and do daily activities. CogniVoice uses speech and text to figure out how well someone’s brain is working. It’s better than other methods at predicting the results of a test called the Mini-Mental State Examination (MMSE). This helps doctors make more accurate diagnoses and develop better treatment plans. |
Keywords
* Artificial intelligence * Classification * Regression
