Artificial intelligence enabled smart mask for speech recognition for future hearing devices

Hameed, Hira; Lubna; Usman, Muhammad; Kazim, Jalil Ur Rehman; Assaleh, Khaled; Arshad, Kamran; Hussain, Amir; Imran, Muhammad; Abbasi, Qammer H.

Abstract

In recent years, Lip-reading has emerged as a significant research challenge. The aim is to recognise speech by analysing Lip movements. The majority of Lip-reading technologies are based on cameras and wearable devices. However, these technologies have well-known occlusion and ambient lighting limitations, privacy concerns as well as wearable device discomfort for subjects and disturb their daily routines. Furthermore, in the era of coronavirus (COVID-19), where face masks are the norm, vision-based and wearable-based technologies for hearing aids are ineffective. To address the fundamental limitations of camera-based and wearable-based systems, this paper proposes a Radio Frequency Identification (RFID)-based smart mask for a Lip-reading framework capable of reading Lips under face masks, enabling effective speech recognition and fostering conversational accessibility for individuals with hearing impairment. The system uses RFID technology to make Radio Frequency (RF) sensing-based Lip-reading possible. A smart RFID face mask is used to collect a dataset containing three different classes of vowels (A, E, I, O, U), Consonants (F, G, M, S), and words (Fish, Goat, Meal, Moon, Snake). The collected data are fed into well-known machine-learning models for classification. A high classification accuracy is achieved by individual classes and combined datasets. On the RFID combined dataset, the Random Forest model achieves a high classification accuracy of 80%.

Citation

Hameed, H., Lubna, Usman, M., Kazim, J. U. R., Assaleh, K., Arshad, K., Hussain, A., Imran, M., & Abbasi, Q. H. (2024). Artificial intelligence enabled smart mask for speech recognition for future hearing devices. Scientific Reports, 14(1), Article 30112. https://doi.org/10.1038/s41598-024-81904-y

• Journal Article Type: Article
• Acceptance Date: Nov 29, 2024
• Online Publication Date: Dec 3, 2024
• Publication Date: 2024
• Deposit Date: Dec 17, 2024
• Publicly Available Date: Dec 17, 2024
• Journal: Scientific Reports
• Electronic ISSN: 2045-2322
• Publisher: Nature Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 1
• Article Number: 30112
• DOI: https://doi.org/10.1038/s41598-024-81904-y