A Survey of Automatic Speech Recognition for Dysarthric Speech

Zhaopeng Qian, Kejing Xiao

Electronics 2023 article cited by 43

Source Open-access PDF More in Medicine & Health

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

Problem

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

Why it matters

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

What the AI did

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.

Results

not stated in source

Impact

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

Record

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

Importance rating

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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