Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Dysarthria, a motor speech disorder caused by neurological impairments, affects speech intelligibility due to weakened articulatory muscles. Traditional speech therapy relies on labor-intensive, in-person sessions with speech-language pathologists (SLPs), posing accessibility challenges. This paper presents an AI-driven therapy system that automates dysarthria severity classification and delivers personalized exercises via an interactive chatbot. Our approach combines acoustic feature extraction (MFCC, Chroma, Spectral Contrast) with a Deep Neural Network (DNN) model, achieving 98% AUC in severity detection. The system includes a Streamlit-based web interface for real-time feedback, progress tracking, and adaptive exercise generation. Evaluated on the TORGO dataset, the solution demonstrates 96% accuracy and scalability, addressing critical gaps in remote speech rehabilitation. This work bridges AI and clinical speech therapy, offering a cost-effective, accessible alternative to traditional methods.
"Personalized AI-Powered Therapy for Dysarthria", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 8, page no.a164-a168, August-2025, Available :http://www.ijrti.org/papers/IJRTI2508026.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator