Vowel Onset Point Based Screening of Misarticulated Stops in Cleft Lip and Palate Speech

Mathad, Vikram C.; Prasanna, S. R. Mahadeva

doi:10.1109/taslp.2019.2957887

Cited by 5 publications

(3 citation statements)

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“…Studies also focused on phoneme detection ( 42 ) and their analysis ( 47 ) to assess the quality of speech. Evaluation of misarticulated stops ( 39 ), intelligibility ( 48 ) and resonance ( 45 ) was done using different models. Two studies detected consonant misarticulation ( 45 , 46 ) in participants with cleft lip and palate.…”

Section: Resultsmentioning

confidence: 99%

“…These provide encouraging experimental results, but they do not reflect the practical performance of these systems. They characterize speech based on hypernasality ( 40 , 41 , 43 , 44 , 46 , 48 ), identifying phonemes ( 42 , 47 ) and misarticulations ( 39 , 45 ). These studies use features of speech data at a single point in time.…”

Section: Discussionmentioning

confidence: 99%

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Current Applications of Artificial Intelligence in Cleft Care: A Scoping Review

et al. 2021

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Objective: This scoping review aims to identify the various areas and current status of the application of artificial intelligence (AI) for aiding individuals with cleft lip and/or palate.Introduction: Cleft lip and/or palate contributes significantly toward the global burden on the healthcare system. Artificial intelligence is a technology that can help individuals with cleft lip and/or palate, especially those in areas with limited access to receive adequate care.Inclusion Criteria: Studies that used artificial intelligence to aid the diagnosis, treatment, or its planning in individuals with cleft lip and/or palate were included.Methodology: A search of the Pubmed, Embase, and IEEE Xplore databases was conducted using search terms artificial intelligence and cleft lip and/or palate. Gray literature was searched using Google Scholar. The study was conducted according to the PRISMA- ScR guidelines.Results: The initial search identified 458 results, which were screened based on title and abstracts. After the screening, removal of duplicates, and a full-text reading of selected articles, 26 publications were included. They explored the use of AI in cleft lip and/or palate to aid in decisions regarding diagnosis, treatment, especially speech therapy, and prediction.Conclusion: There is active interest and immense potential for the use of artificial intelligence in cleft lip and/or palate. Most studies currently focus on speech in cleft palate. Multi-center studies that include different populations, with collaboration amongst academicians and researchers, can further develop the technology.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Current Applications of Artificial Intelligence in Cleft Care: A Scoping Review

et al. 2021

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“…As an alternative to traditional features based on signal processing, joint spectro-temporal features have been proposed to model CV transition regions in machine learning models. The two-dimensional discrete cosine transform (2D-DCT) is one of the most commonly used approaches to modeling the spectrotemporal dynamics of CV transition regions [19], [22]- [24]. Supervised learning methods using 2D-DCT features as input have been used for classification of place of articulation in stop consonants [23], to evaluate the goodness of /t/ and /k/ productions in children with speech sound disorders [25], to detect stop consonant production errors [24], and to model perceptual intelligibility ratings in children with CP [19].…”

Section: A Related Workmentioning

confidence: 99%

Consonant-Vowel Transition Models Based on Deep Learning for Objective Evaluation of Articulation

Mathad¹,

Liss²,

Chapman³

et al. 2022

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Spectro-temporal dynamics of consonant-vowel (CV) transition regions are considered to provide robust cues related to articulation. In this work, we propose an objective measure of precise articulation, dubbed the objective articulation measure (OAM), by analyzing the CV transitions segmented around vowel onsets. The OAM is derived based on the posteriors of a convolutional neural network pre-trained to classify between different consonants using CV regions as input. We demonstrate the OAM is correlated with perceptual measures in a variety of contexts including (a) adult dysarthric speech, (b) the speech of children with cleft lip/palate, and (c) a database of accented English speech from native Mandarin and Spanish speakers.

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