Adão, TelmoOliveira, JoaoShahrabadi, SomayehJesus, HugoFernandes, Marco Paulo SampaioCosta, AngeloGonçalves, Martinho FradeiraLopez, Miguel A.G.Peres, EmanuelMagalhães, Luis G.2011-06-032011-06-032023Adão, Telmo; Oliveira, Joao; Shahrabadi, Somayeh; Jesus, Hugo; Fernandes, Marco Paulo Sampaio; Costa, Angelo; Gonçalves, Martinho Fradeira; Lopez, Miguel A.G.; Peres, Emanuel; Magalhães, Luis G. (2023). Empowering deaf-hearing communication: exploring synergies between predictive and generative ai-based strategies towards (portuguese) sign language interpretation. Journal of Imaging. eISSN 2313-433X. 9:11, p. 1-30http://hdl.handle.net/10198/4957Communication 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.engSign language recognition (SLR)Portuguese sign languageVideo-based motion analyticsMachine learning (ML)Long-short term memory (LSTM)Large language models (LLM)Generative pre-trained transformer (GPT)Deaf-hearing communicationInclusionjournal article10.3390/jimaging91102352313-433X