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This effort addresses the research methodologies required to implement an automated device for detecting
neurodevelopment disorders like depression and autism from acoustic features in speech. The tool is aimed at decreasing the barrier set for seeking help for potential mental disorders and aiding medical diagnoses. Automatic Neurodevelopmental Disorder Detection is a relatively incipient topic. Our research work presents a novel approach focusing on two aspects that receive scant research attention: class imbalance and feature extractions.
Keywords:
Acoustic features of speech, Class Imbalance, Deep Learning, Feature Extraction.
Cite Article:
"Decoding Emotional Responses with Deep Learning using Audio Data to detect Early Onset of Neurodevelopment Disorders", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 6, page no.1516 - 1520, June-2022, Available :http://www.ijrti.org/papers/IJRTI2206223.pdf
Downloads:
000205226
ISSN:
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