The DOI matches its Crossref record.
Detection of articulation disorders using Empirical Mode Decomposition and neural networks
Abstract
This paper introduces a novel approach based on signal processing methods to extract features from speech signals and based on them to detect a specific type of articulation disorders. Articulation, in effect, is the specific and characteristic way that an individual produces the speech sounds. Empirical Mode Decomposition and the Hilbert Huang transform are applied to the speech signal in order to calculate the marginal spectrum of the signal. The marginal spectrum is subsequently subject to a mel-cepstrum like processing to extract features which are fed to a neural network classifier responsible for the identification of the articulation disorder. Our preliminary results suggest that this approach is very promising for the detection of the disorder under study.
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“Multilayer perceptron neural network: Achieved an overall accuracy percentage of 79.86% in 41 study to detect the presence of articulation disorder for those with substitution errors only. However, sensitivity and specificity percentages weren’t reported.”
From The role of AI in the diagnosis of speech and language disorders: A systematic mapping study (2025) · cites this paper as [41]
In the citing paper
“Many AI models have been trained and tested using datasets that are specific to certain languages, dialects, age groups, and/or disorder subtypes, which limits the applicability of the AI models to larger broader populations.16,41,42,55,56,76,95”
From The role of AI in the diagnosis of speech and language disorders: A systematic mapping study (2025) · cites this paper as [41]
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No references could be resolved yet.
Research works printed but not linked: 18
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- georgopoulos, 2001, a computer based speech therapy system for articulation disorders, Proc 4th Int Conf Neural Networks and Expert Systems in Medicine and Healthcare (NNESMED), 223
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- bauman-waengler, 2000, Articulatory and Phonological Impairments A Clinical Focus
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The role of AI in the diagnosis of speech and language disorders: A systematic mapping study
Tbaishat, D., Al-Shafei, R., & Odeh, M. (2025) · DIGITAL HEALTH · doi:10.1177/20552076251379769 · Cited by 4
Metadata
- Authors
- Georgoulas, G., Georgopoulos, V. C., Stylios, G. D., & Stylios, C. D.
- Affiliations
- Technological Educational Institute of the Ionian Islands (GR)
- Technological Educational Institute of Epirus (GR)
- Venue
- 2009 International Joint Conference on Neural Networks
- Publisher
- IEEE
- Published
- 2009-06
- Volume
- 35
- Pages
- 959-964
- ISSN
- 2161-4407 (ISSN-L)
- OpenAlex
- W2097409787
- Cited by
- 3
- References
- 0
- Citations by year
- 2013: 1 · 2012: 2
- FWCI
- 6.50
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
- Topics
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- articulation disorders, speech processing, empirical mode decomposition, Hilbert-Huang transform, marginal spectrum, mel-cepstrum, feature extraction, neural network classification
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