Deep learning-based acoustic feature representations for dysarthric speech recognition

M Latha, M Shivakumar, G Manjula, M Hemakumar… - SN Computer …, 2023 - Springer
Dysarthria is a motor speech disorder and the most common neurodegenerative disease
characterized by low volume in precise articulation, poor coordination of respiratory and …

[HTML][HTML] Dysarthria speech detection using convolutional neural networks with gated recurrent unit

DH Shih, CH Liao, TW Wu, XY Xu, MH Shih - Healthcare, 2022 - mdpi.com
In recent years, due to the rise in the population and aging, the prevalence of neurological
diseases is also increasing year by year. Among these patients with Parkinson's disease …

Toward a lightweight ASR solution for atypical speech on the edge

D Mulfari, L Carnevale, M Villari - Future Generation Computer Systems, 2023 - Elsevier
In this article, to the purpose of simplifying challenges in designing automatic speech
recognition (ASR) systems working on disordered speech, we focus on an isolated word …

[PDF][PDF] Bayesian Parametric and Architectural Domain Adaptation of LF-MMI Trained TDNNs for Elderly and Dysarthric Speech Recognition.

J Deng, FR Gutierrez, S Hu, M Geng, X Xie, Z Ye… - Interspeech, 2021 - se.cuhk.edu.hk
Automatic recognition of elderly and disordered speech remains a highly challenging task to
date. Such data is not only difficult to collect in large quantities, but also exhibits a significant …

[HTML][HTML] Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

R Maskeliūnas, R Damaševičius, A Kulikajevas… - Cancers, 2023 - mdpi.com
Simple Summary This paper introduces a new method for cleaning impaired speech by
combining Pareto-optimized deep learning with Non-negative Matrix Factorization (NMF) …

Development of CNN-based robust dysarthric isolated digit recognition system by enhancing speech intelligibility

A Revathi, N Sasikaladevi, D Arunprasanth - Research on Biomedical …, 2022 - Springer
Purpose Developing a computer-assisted speech training/recognition system for
recognizing the speeches of dysarthric speakers has become necessary because their …

[HTML][HTML] Combined convolution recurrent neural network for the classification of dysarthria speech

M Mahendran, R Visalakshi… - International Journal of …, 2024 - journals.lww.com
Dysarthria is a neuromotor articulation condition that affects a person and weakens their
tongue and lip muscles, and it additionally affects their capability to talk. Diffusion of factors …

[PDF][PDF] Application of Fractal Analysis based Feature Extractor for Channel Reduction of Silent Speech Interface Using Facial Electromyography

A Abdullah, OS Powar, K Chemmangat - International Journal of …, 2023 - inass.org
Surface electromyography (sEMG) based silent speech interface (SSI) is an actively
investigated topic among the broad area of human computer interaction studies which is …

An Approach to Recognize Speech Using Convolutional Neural Network for the Multilingual Language

A Gupta, R Kumar, M Gupta - 2023 Global Conference on …, 2023 - ieeexplore.ieee.org
Automatic Speech Recognition Systems (ASRS) are essential for supporting natural
language communication between human and machines. It has gained prominence when …

Experimental Results of CNN and RNN Models for Identification of Parkinson's Disease

M Mahendran, R Visalakshi - 2023 First International …, 2023 - ieeexplore.ieee.org
There are many people with neurological disorders, and these disorders lead to dysarthria.
Dysarthria, makes it more difficult for croakers to control the issue if not addressed promptly …