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    This study introduces a new markerless 3D lip tracking method using AI. The approach achieves high precision, comparable to existing methods, and works well for both adults and young children.

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    Area of Science:

    • Speech Science
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Accurate measurement of speech movements is crucial but challenging.
    • Current methods often require physical sensors, which can be uncomfortable for children.
    • Markerless facial tracking shows promise but lacks validated 3D precision.

    Purpose of the Study:

    • To develop and validate a novel markerless 3D lip tracking framework.
    • To assess the precision and accuracy of the proposed method.
    • To evaluate performance across different age groups.

    Main Methods:

    • Integration of a facial landmark detector with CoTracker, a transformer-based neural network.
    • Joint tracking of dense facial points across video sequences.
    • Validation against electromagnetic articulography data.

    Main Results:

    • The novel approach achieved high precision (≈ 0.15 mm SD), outperforming facial landmark detection alone (> 0.3 mm SD).
    • 3D tracking accuracy was comparable to electromagnetic articulography (≈ 0.3 mm RMSE).
    • Consistent performance was observed in adults and young children (3-4 years old).

    Conclusions:

    • The developed framework offers a precise and accurate markerless 3D lip tracking solution.
    • This method is suitable for diverse populations, including young children.
    • The approach promotes open science and has potential to enhance various markerless tracking applications in neuroscience.