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    Area of Science:

    • Speech processing
    • Machine learning
    • Audio analysis

    Background:

    • The COVID-19 pandemic highlighted the need for detecting face mask usage during speech.
    • Face masks alter speech characteristics, including temporal aspects like pace and rhythm.
    • Accurate detection of mask-wearing from audio is crucial for public health monitoring.

    Purpose of the Study:

    • To develop and evaluate effective neural network models for detecting surgical masks from audio signals.
    • To compare the performance of different deep learning architectures, including CNNs, LSTMs, and Transformers.
    • To investigate the impact of hybrid models and data augmentation on mask detection accuracy.

    Main Methods:

    • Proposed two Convolutional Neural Network (CNN)-based architectures: one incorporating Long Short-Term Memory (LSTM) with an attention mechanism, and another (ConvTx) using a Transformer module.
    • Developed three hybrid models combining LSTM and Transformer components to leverage their complementary strengths in modeling temporal dynamics.
    • Explored data augmentation techniques, including audio frame transitions and gender-dependent frameworks.

    Main Results:

    • One of the hybrid models demonstrated superior performance in detecting surgical masks from audio.
    • The proposed models, particularly the hybrid approach, surpassed existing state-of-the-art results for this task.
    • Experimental results indicated the effectiveness of combining different neural network architectures for improved temporal modeling.

    Conclusions:

    • Hybrid deep learning models integrating CNNs, LSTMs, and Transformers are highly effective for surgical mask detection from speech.
    • The developed models offer a promising solution for automated, non-intrusive monitoring of mask-wearing in public health contexts.
    • Further research into data augmentation and model architectures can continue to enhance the accuracy and robustness of audio-based mask detection systems.