A Survey of Automatic Speech Recognition for Dysarthric Speech

Zhaopeng Qian, Kejing Xiao

Electronics 2023 article cited by 43

importance 5/10 ai assisted Speech Recognition and Synthesis assistive speech technology stage: research

People with dysarthric speech (discontinuous, uncontrolled volume, slow or explosive pronunciation) are poorly served by standard automatic speech recognition.

Reliable ASR can restore communication and independence for speakers with dysarthria.

This is a scoping survey (1990-2022) documenting how deep learning methods such as CNNs, DNNs and RNNs have been applied to acoustic, lexical and language models to improve dysarthric-speech ASR.

not stated in source

The survey reports deep learning was widely applied in the 2010s to improve ASR performance, but cites no concrete benchmark figures.

Venue Electronics
Published 2023
Work type article
Authors Zhaopeng Qian, Kejing Xiao
Citations 43 (OpenAlex, at import time)
References 85
Model or method convolutional neural networks, deep neural networks, recurrent neural networks
Organization not stated in source
Primary source https://doi.org/10.3390/electronics12204278
Open access gold
Keywords dysarthric speech, automatic speech recognition, deep learning, survey, CNN, RNN
5 / 10

real but narrower advance

ASR for dysarthric speakers could restore communication and independence for a disability community, but the abstract is a scoping review citing no scale or benchmark figures.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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