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

• Published in: Scientific reports 2024-12, Vol.14 (1), p.30112-11
• Main Authors: Hameed, Hira, Lubna, Usman, Muhammad, Kazim, Jalil Ur Rehman, Assaleh, Khaled, Arshad, Kamran, Hussain, Amir, Imran, Muhammad, Abbasi, Qammer H.
• Format: Article
• Language: English
• Subjects: 639/166; 692/700; Artificial Intelligence; Cameras; Classification; Coronaviruses; COVID-19; Face; Hearing Aids; Hearing loss; Hearing Loss - rehabilitation; Humanities and Social Sciences; Humans; Lipreading; Machine Learning; Masks; multidisciplinary; Pattern recognition; Protective equipment; Radio frequency identification; Radio Frequency Identification Device - methods; Radio-tagging; SARS-CoV-2; Science; Science (multidisciplinary); Speech; Speech recognition; Speech Recognition Software; Voice recognition; Wearable Electronic Devices
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-81904-y

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%.