Florescer entre Sons e Silêncios: Ferramenta de Apoio à Classificação de Vocalizações Não Verbais com Inteligência Artificial - Silva, Fernanda Floriano; Macedo, Alessandra Alaniz (Anais Estendidos do Simpósio Brasileiro de Sistemas Multimídia e Web (WebMedia); 2025: Anais Estendidos do XXXI Simpósio Brasileiro de Sistemas Multimídia e WebDO - 10.5753/webmedia_estendido.2025.16396, 2025)
Communication, a fundamental human right, can be compromised in neurodevelopmental disorders, such as in nonverbal children with Autism Spectrum Disorder, whose vocalizations often lack intelligibility. This study explores how artificial intelligence can support phonological analysis in this context. A Brazilian Portuguese dataset was built, combining reference phonemes — processed with acoustic feature extraction and data augmentation — and vocalizations from a nonverbal child after preprocessing. Unsupervised methods revealed consistent phonological approximations, particularly in nasal categories. In the supervised analysis, samples were represented through Bag-of-Audio-Words (BoAW) combined with acoustic features, and class imbalance was addressed using SMOTE. The evaluated models included KNN, RF, MLP, SVM, and CNN. Results showed that SVM achieved the best performance in terms of phonetic/articulatory equivalences, RF demonstrated robustness in unbalanced scenarios, and CNN reached high accuracy on the validation set. Comparison with perceptual-auditory analyses by speech therapists confirmed relevant convergences. These findings highlight the feasibility of computational models as complementary resources to clinical listening, supporting therapeutic interventions and the development of child speech.