Research in methodologies for modelling the oral cavity.

AI in the physical world clinical phonetics vocal tract research

Journal

Biomedical physics & engineering express
ISSN: 2057-1976
Titre abrégé: Biomed Phys Eng Express
Pays: England
ID NLM: 101675002

Informations de publication

Date de publication:
18 Mar 2024
Historique:
received: 15 08 2023
accepted: 13 02 2024
pubmed: 13 2 2024
medline: 13 2 2024
entrez: 13 2 2024
Statut: epublish

Résumé

The paper aims to explore the current state of understanding surrounding in silico oral modelling. This involves exploring methodologies, technologies and approaches pertaining to the modelling of the whole oral cavity; both internally and externally visible structures that may be relevant or appropriate to oral actions. Such a model could be referred to as a 'complete model' which includes consideration of a full set of facial features (i.e. not only mouth) as well as synergistic stimuli such as audio and facial thermal data. 3D modelling technologies capable of accurately and efficiently capturing a complete representation of the mouth for an individual have broad applications in the study of oral actions, due to their cost-effectiveness and time efficiency. This review delves into the field of clinical phonetics to classify oral actions pertaining to both speech and non-speech movements, identifying how the various vocal organs play a role in the articulatory and masticatory process. Vitaly, it provides a summation of 12 articulatory recording methods, forming a tool to be used by researchers in identifying which method of recording is appropriate for their work. After addressing the cost and resource-intensive limitations of existing methods, a new system of modelling is proposed that leverages external to internal correlation modelling techniques to create a more efficient models of the oral cavity. The vision is that the outcomes will be applicable to a broad spectrum of oral functions related to physiology, health and wellbeing, including speech, oral processing of foods as well as dental health. The applications may span from speech correction, designing foods for the aging population, whilst in the dental field we would be able to gain information about patient's oral actions that would become part of creating a personalised dental treatment plan.

Identifiants

pubmed: 38350128
doi: 10.1088/2057-1976/ad28cc
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Creative Commons Attribution license.

Auteurs

Muhammad Suhaib Shahid (MS)

School of Computer Science, University of Nottingham, NG8 1BB, United Kingdom.

Andrew P French (AP)

School of Computer Science, University of Nottingham, NG8 1BB, United Kingdom.
School of Biosciences, University of Nottingham, LE12 5RD, United Kingdom.

Michel F Valstar (MF)

School of Computer Science, University of Nottingham, NG8 1BB, United Kingdom.

Gleb E Yakubov (GE)

School of Biosciences, University of Nottingham, LE12 5RD, United Kingdom.

Classifications MeSH