ArticleIEEE/ACM transactions on audio, speech, and language processing2023
Consonant-Vowel Transition Models Based on Deep Learning for Objective Evaluation of Articulation.
Article in IEEE/ACM transactions on audio, speech, and language processing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- An End-to-End Overview of Clinical Speech AI.IEEE transactions on audio, speech, and language processing (2025) · 2026Article
- Artificial intelligence applications in the diagnosis and management of cleft lip and palate: An updated review.Journal of dental sciences · 2026Review
- Artificial Intelligence Applications in Pediatric Craniofacial Surgery.Diagnostics (Basel, Switzerland) · 2025Review
- Responsible development of clinical speech AI: Bridging the gap between clinical research and technology.NPJ digital medicine · 2024Review
- Dysarthria detection based on a deep learning model with a clinically-interpretable layer.JASA express letters · 2023Article
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Authors and funding
5 authors.
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Abstract
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 that 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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