TY  - EJOU
AU  - Innocente, Chiara 
AU  - Boemio, Matteo 
AU  - Lorenzetti, Gianmarco 
AU  - Pulito, Ilaria 
AU  - Romagnoli, Diego 
AU  - Saponaro, Valeria 
AU  - Marullo, Giorgia 
AU  - Ulrich, Luca 
AU  - Vezzetti, Enrico 

TI  - Deep Learning-Based Lip-Reading for Vocal Impaired Patient Rehabilitation
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 143
IS  - 2
SN  - 1526-1506

AB  - Lip-reading technology, based on visual speech decoding and automatic speech recognition, offers a promising solution to overcoming communication barriers, particularly for individuals with temporary or permanent speech impairments. However, most Visual Speech Recognition (VSR) research has primarily focused on the English language and general-purpose applications, limiting its practical applicability in medical and rehabilitative settings. This study introduces the first Deep Learning (DL) based lip-reading system for the Italian language designed to assist individuals with vocal cord pathologies in daily interactions, facilitating communication for patients recovering from vocal cord surgeries, whether temporarily or permanently impaired. To ensure relevance and effectiveness in real-world scenarios, a carefully curated vocabulary of twenty-five Italian words was selected, encompassing critical semantic fields such as Needs, Questions, Answers, Emergencies, Greetings, Requests, and Body Parts. These words were chosen to address both essential daily communication and urgent medical assistance requests. Our approach combines a spatiotemporal Convolutional Neural Network (<i>CNN</i>) with a bidirectional Long Short-Term Memory (<i>BiLSTM</i>) recurrent network, and a Connectionist Temporal Classification (CTC) loss function to recognize individual words, without requiring predefined words boundaries. The experimental results demonstrate the system’s robust performance in recognizing target words, reaching an average accuracy of 96.4% in individual word recognition, suggesting that the system is particularly well-suited for offering support in constrained clinical and caregiving environments, where quick and reliable communication is critical. In conclusion, the study highlights the importance of developing language-specific, application-driven VSR solutions, particularly for non-English languages with limited linguistic resources. By bridging the gap between deep learning-based lip-reading and real-world clinical needs, this research advances assistive communication technologies, paving the way for more inclusive and medically relevant applications of VSR in rehabilitation and healthcare.
KW  - Lip-reading; deep learning; automatic speech recognition; visual speech decoding; 3D convolutional neural network

DO  - 10.32604/cmes.2025.063186