Enhancing dysarthric speech recognition through SepFormer and hierarchical attention network models with multistage transfer learning

Enhancing dysarthric speech recognition through SepFormer and hierarchical attention network models with multistage transfer learning

Abstract Dysarthria, a motor speech disorder that impacts articulation and speech clarity, presents significant challenges for Automatic Speech Recognition (ASR) systems. This study proposes a groundbreaking approach to enhance the accuracy of Dysarthric Speech Recognition (DSR). A primary innovatio...

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Bibliographic Details
Main Authors: R. Vinotha, D. Hepsiba, L. D. Vijay Anand, J. Andrew, R. Jennifer Eunice
Format: Article
Language:English
Published: Nature Portfolio 2024-11-01
Series:Scientific Reports
Subjects:
SepFormer-SEGAN
HAN
Transformer
Conformer
DSR
Dysarthric speech enhancement
Online Access:https://doi.org/10.1038/s41598-024-80764-w
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https://doi.org/10.1038/s41598-024-80764-w

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