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E2E-DASR: End-to-end deep learning-based dysarthric automatic speech recognition by Almadhor, Ahmad (author);Irfan, Rizwana (author);Gao, Jiechao (author);Saleem, Nasir (author);Tayyab Rauf, Hafiz (author);Kadry, Seifedine (author) is a Computer Science article available to read on EtoBox.

What is E2E-DASR: End-to-end deep learning-based dysarthric automatic speech recognition about?

Dysarthria is a motor speech disability caused by weak muscles and organs involved in the articulation process, thereby affecting the speech intelligibility of individuals. Because this condition is linked to physical exhaustion disabilities, individuals not only have communication difficulties, but also have difficulty interacting with digital devices. Automatic speech recognition (ASR) makes an important difference for individuals with dysarthria since modern digital devices offer a better interaction medium that enables them to interact with their community and computers. Still, the performance of ASR technologies is poor in recognizing dysarthric speech, particularly for acute dysarthria. Multiple challenges, including dysarthric phoneme inaccuracy and labeling imperfection, are facing dysarthric ASR technologies. This paper proposes a spatio-temporal dysarthric ASR (DASR) system using Spatial Convolutional Neural Network (SCNN) and Multi-Head Attention Transformer (MHAT) to visually extract the speech features, and DASR learns the shapes of phonemes pronounced by dysarthric individuals. This visual DASR feature modeling eliminates phoneme-related challenges. The UA-Speech data

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Author

Almadhor, Ahmad (author);Irfan, Rizwana (author);Gao, Jiechao (author);Saleem, Nasir (author);Tayyab Rauf, Hafiz (author);Kadry, Seifedine (author)

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)