Facial Movements Extracted from Video for the Kinematic Classification of Speech.

Autor: Palmer R; School of Earth and Planetary Sciences, Curtin University, Perth, WA 6102, Australia.; School of Allied Health, Curtin University, Perth, WA 6102, Australia., Ward R; School of Allied Health, Curtin University, Perth, WA 6102, Australia., Helmholz P; School of Earth and Planetary Sciences, Curtin University, Perth, WA 6102, Australia., Strauss GR; School of Allied Health, Curtin University, Perth, WA 6102, Australia., Davey P; School of Allied Health, Curtin University, Perth, WA 6102, Australia., Hennessey N; School of Allied Health, Curtin University, Perth, WA 6102, Australia., Orton L; School of Allied Health, Curtin University, Perth, WA 6102, Australia., Namasivayam A; Department of Speech-Language Pathology, University of Toronto, Toronto, ON M5G 1V7, Canada.
Jazyk: angličtina
Zdroj: Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Nov 12; Vol. 24 (22). Date of Electronic Publication: 2024 Nov 12.
DOI: 10.3390/s24227235
Abstrakt: Speech Sound Disorders (SSDs) are prevalent communication problems in children that pose significant barriers to academic success and social participation. Accurate diagnosis is key to mitigating life-long impacts. We are developing a novel software solution-the Speech Movement and Acoustic Analysis Tracking (SMAAT) system to facilitate rapid and objective assessment of motor speech control issues underlying SSD. This study evaluates the feasibility of using automatically extracted three-dimensional (3D) facial measurements from single two-dimensional (2D) front-facing video cameras for classifying speech movements. Videos were recorded of 51 adults and 77 children between 3 and 4 years of age (all typically developed for age) saying 20 words from the mandibular and labial-facial levels of the Motor-Speech Hierarchy Probe Wordlist (MSH-PW). Measurements around the jaw and lips were automatically extracted from the 2D video frames using a state-of-the-art facial mesh detection and tracking algorithm, and each individual measurement was tested in a Leave-One-Out Cross-Validation (LOOCV) framework for its word classification performance. Statistics were evaluated at the α=0.05 significance level and several measurements were found to exhibit significant classification performance in both the adult and child cohorts. Importantly, measurements of depth indirectly inferred from the 2D video frames were among those found to be significant. The significant measurements were shown to match expectations of facial movements across the 20 words, demonstrating their potential applicability in supporting clinical evaluations of speech production.
Databáze: MEDLINE
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