Adão, TelmoOliveira, JoãoShahrabadi, SomayehJesus, HugoFernandes, MarcoCosta, ÂngeloFerreira, VâniaGonçalves, Martinho FradeiraGuevara Lopez, Miguel AngelPeres, Emanuel2023-11-032023-11-032023Adão, T., Oliveira, J., Shahrabadi, S., Jesus, H., Fernandes, M., Costa, Â., Ferreira, V., et al. (2023). Empowering Deaf-Hearing Communication: Exploring Synergies between Predictive and Generative AI-Based Strategies towards (Portuguese) Sign Language Interpretation. Journal of Imaging, 9(11), 235. https://doi.org/10.3390/jimaging91102352313-433Xhttp://hdl.handle.net/10400.26/47818Communication between Deaf and hearing individuals remains a persistent challenge requiring attention to foster inclusivity. Despite notable efforts in the development of digital solutions for sign language recognition (SLR), several issues persist, such as cross-platform interoperability and strategies for tokenizing signs to enable continuous conversations and coherent sentence construction. To address such issues, this paper proposes a non-invasive Portuguese Sign Language (Língua Gestual Portuguesa or LGP) interpretation system-as-a-service, leveraging skeletal posture sequence inference powered by long-short term memory (LSTM) architectures. To address the scarcity of examples during machine learning (ML) model training, dataset augmentation strategies are explored. Additionally, a buffer-based interaction technique is introduced to facilitate LGP terms tokenization. This technique provides real-time feedback to users, allowing them to gauge the time remaining to complete a sign, which aids in the construction of grammatically coherent sentences based on inferred terms/words. To support human-like conditioning rules for interpretation, a large language model (LLM) service is integrated. Experiments reveal that LSTM-based neural networks, trained with 50 LGP terms and subjected to data augmentation, achieved accuracy levels ranging from 80% to 95.6%. Users unanimously reported a high level of intuition when using the buffer-based interaction strategy for terms/words tokenization. Furthermore, tests with an LLM—specifically ChatGPT—demonstrated promising semantic correlation rates in generated sentences, comparable to expected sentences.engjournal articlehttps://doi.org/10.3390/jimaging9110235