The DOI matches its Crossref record.
Speech Sound Classification and Detection of Articulation Disorders with Support Vector Machines and Wavelets
Abstract
This paper proposes a novel integrated methodology to extract features and classify speech sounds with intent to detect the possible existence of a speech articulation disorder in a speaker. Articulation, in effect, is the specific and characteristic way that an individual produces the speech sounds. A methodology to process the speech signal, extract features and finally classify the signal and detect articulation problems in a speaker is presented. The use of support vector machines (SVMs), for the classification of speech sounds and detection of articulation disorders is introduced. The proposed method is implemented on a data set where different sets of features and different schemes of SVMs are tested leading to satisfactory performance.
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“However, the diagnosis of articulation disorder is very difficult and relies on the expertise of the speech-language pathologist. 16”
From The role of AI in the diagnosis of speech and language disorders: A systematic mapping study (2025) · cites this paper as [16]
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“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 [16]
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Research works printed but not linked: 18
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Sorted by citations
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, C. D.
- Affiliations
- University of Patras (GR)
- Technological Educational Institute of Epirus (GR)
- Venue
- 2006 International Conference of the IEEE Engineering in Medicine and Biology Society
- Publisher
- IEEE
- Published
- 2006-08
- Volume
- 2006
- Pages
- 2199-2202
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W2097180381
- Cited by
- 24
- References
- 0
- Citations by year
- 2025: 1 · 2024: 4 · 2023: 3 · 2022: 3 · 2021: 2 · 2020: 2 · 2019: 1 · 2016: 1 · 2014: 1 · 2013: 1 · 2012: 1
- FWCI
- 7.35
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Phonetics and Phonology Research
- Keywords
- speech sound classification, articulation disorders, support vector machine, wavelet features, feature extraction, speech processing
- MeSH terms
- Algorithms, Articulation Disorders, Artificial Intelligence, Child, Diagnosis, Computer-Assisted, Female, Humans, Male, Pattern Recognition, Automated, Sensitivity and Specificity, Sound Spectrography, Speech Production Measurement, Reproducibility of Results, Speech Recognition Software
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- CoChat. (2026). Citation check: Speech Sound Classification and Detection of Articulation Disorders with Support Vector Machines and Wavelets. https://app.cochat.ai/papers/10.1109/iembs.2006.259499
- The paper
- Georgoulas, G., Georgopoulos, V. C., & Stylios, C. D. (2006). Speech Sound Classification and Detection of Articulation Disorders with Support Vector Machines and Wavelets. 2006 International Conference of the IEEE Engineering in Medicine and Biology Society. https://doi.org/10.1109/iembs.2006.259499
