Summary of Empowering Dysarthric Speech: Leveraging Advanced Llms For Accurate Speech Correction and Multimodal Emotion Analysis, by Kaushal Attaluri et al.
Empowering Dysarthric Speech: Leveraging Advanced LLMs for Accurate Speech Correction and Multimodal Emotion Analysis
by Kaushal Attaluri, Anirudh CHVS, Sireesha Chittepu
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper introduces a novel approach to recognizing and translating dysarthric speech, leveraging advanced large language models for accurate speech correction and multimodal emotion analysis. The system first converts dysarthric speech to text using OpenAI Whisper model, followed by sentence prediction using fine-tuned open-source models. The framework identifies emotions such as happiness, sadness, neutrality, surprise, anger, and fear, while reconstructing intended sentences from distorted speech with high accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This approach helps individuals with dysarthria communicate more effectively, overcoming the major communication barrier caused by this motor speech disorder. Dysarthria affects millions of people worldwide, including those with conditions such as stroke, traumatic brain injury, cerebral palsy, Parkinson’s disease, and multiple sclerosis. The proposed system has significant advancements in the recognition and interpretation of dysarthric speech. |
