Articulation Analysis in the Speech of Children with Cleft Lip and Palate

Carvajal-Castaño, Helber A.; Orozco-Arroyave, Juan Rafael

doi:10.1007/978-3-030-33904-3_54

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…The authors considered the Voice Low tone to High tone Ratio (VLHR) as a measure of nasality, finding a significant decreasing in the VLHR index (at 95% confidence level) after surgery for the sustained phonation of vowels. Classical articulatory and spectral features were considered in [4,12] to classify between normal speech and hypernasal speech in children with CLP, achieving accuracies of up to 85% and 80%, respectively.…”

Section: Introductionmentioning

confidence: 99%

50 Shades of Gray: Effect of the Color Scale for the Assessment of Speech Disorders

Pérez-Toro

Klumpp

Vásquez-Correa

et al. 2022

Text, Speech, and Dialogue

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Spectrograms provide a visual representation of the timefrequency variations of a speech signal. Furthermore, the color scales can be used as a pre-processing normalization step. In this study, we investigated the suitability of using different color scales for the reconstruction of spectrograms together with bottleneck features extracted from Convolutional AutoEncoders (CAEs). We trained several CAEs considering different parameters such as the number of channels, wideband/narrowband spectrograms, and different color scales. Additionally, we tested the suitability of the proposed CAE architecture for the prediction of the severity of Parkinson's Disease (PD) and for the nasality level in children with Cleft Lip and Palate (CLP). The results showed that it is possible to estimate the neurological state for PD with Spearman's correlations of up to 0.71 using the Grayscale, and the nasality level in CLP with F-scores of up to 0.58 using the raw spectrogram. Although the color scales improved performance in some cases, it is not clear which color scale is the most suitable for the selected application, as we did not find significant differences in the results for each color scale.

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Section: Introductionmentioning

confidence: 99%

50 Shades of Gray: Effect of the Color Scale for the Assessment of Speech Disorders

Pérez-Toro

Klumpp

Vásquez-Correa

et al. 2022

Text, Speech, and Dialogue

Self Cite

View full text Add to dashboard Cite

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Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate—A Systematic Review

Huqh

Abdullah

Wong

et al. 2022

IJERPH

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Objective: The objective of this systematic review was (a) to explore the current clinical applications of AI/ML (Artificial intelligence and Machine learning) techniques in diagnosis and treatment prediction in children with CLP (Cleft lip and palate), (b) to create a qualitative summary of results of the studies retrieved. Materials and methods: An electronic search was carried out using databases such as PubMed, Scopus, and the Web of Science Core Collection. Two reviewers searched the databases separately and concurrently. The initial search was conducted on 6 July 2021. The publishing period was unrestricted; however, the search was limited to articles involving human participants and published in English. Combinations of Medical Subject Headings (MeSH) phrases and free text terms were used as search keywords in each database. The following data was taken from the methods and results sections of the selected papers: The amount of AI training datasets utilized to train the intelligent system, as well as their conditional properties; Unilateral CLP, Bilateral CLP, Unilateral Cleft lip and alveolus, Unilateral cleft lip, Hypernasality, Dental characteristics, and sagittal jaw relationship in children with CLP are among the problems studied. Results: Based on the predefined search strings with accompanying database keywords, a total of 44 articles were found in Scopus, PubMed, and Web of Science search results. After reading the full articles, 12 papers were included for systematic analysis. Conclusions: Artificial intelligence provides an advanced technology that can be employed in AI-enabled computerized programming software for accurate landmark detection, rapid digital cephalometric analysis, clinical decision-making, and treatment prediction. In children with corrected unilateral cleft lip and palate, ML can help detect cephalometric predictors of future need for orthognathic surgery.

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Articulation Analysis in the Speech of Children with Cleft Lip and Palate

Cited by 2 publications

References 10 publications

50 Shades of Gray: Effect of the Color Scale for the Assessment of Speech Disorders

50 Shades of Gray: Effect of the Color Scale for the Assessment of Speech Disorders

Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate—A Systematic Review

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