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    <abstract>Speech-language assessment of stuttering is traditionally manual, subjective, and time-consuming. This paper presents the development of software for automatic detection and classification of stuttering-related disfluencies in Brazilian Portuguese, aiming to support clinical assessment. The system follows a two-stage hybrid approach. In the first stage, it applies deterministic algorithms based on automatic speech recognition (ASR) and temporal information to identify simple disfluencies, such as repetitions and pauses. In the second stage, it employs a hierarchical architecture combining a Kohonen network (Self-Organizing Map, SOM) and a Multilayer Perceptron (MLP) to classify complex disfluencies, specifically blocks and prolongations, using acoustic features. Because no publicly available annotated resources exist for this task in Brazilian Portuguese, we built a initial dataset annotated by specialists. The system achieved 89.5% accuracy in classifying complex disfluencies, with a Matthews Correlation Coeficient (MCC) of 0.812. These results indicate the feasibility of the tool as decision support for clinical assessment and establish a baseline for future research.</abstract>
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%0 Conference Proceedings
%T Detecting Stuttering with Artificial Intelligence: A Hybrid Method for Brazilian Portuguese
%A Buzzeti, Rubens Perini
%A Buzzeti, Paula Bianca Meireles de Moura
%A Santos, Roney Lira de Sales
%Y Souza, Marlo
%Y de-Dios-Flores, Iria
%Y Santos, Diana
%Y Freitas, Larissa
%Y Souza, Jackson Wilke da Cruz
%Y Ribeiro, Eugénio
%S Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1
%D 2026
%8 April
%I Association for Computational Linguistics
%C Salvador, Brazil
%@ 979-8-89176-387-6
%F buzzeti-etal-2026-detecting
%X Speech-language assessment of stuttering is traditionally manual, subjective, and time-consuming. This paper presents the development of software for automatic detection and classification of stuttering-related disfluencies in Brazilian Portuguese, aiming to support clinical assessment. The system follows a two-stage hybrid approach. In the first stage, it applies deterministic algorithms based on automatic speech recognition (ASR) and temporal information to identify simple disfluencies, such as repetitions and pauses. In the second stage, it employs a hierarchical architecture combining a Kohonen network (Self-Organizing Map, SOM) and a Multilayer Perceptron (MLP) to classify complex disfluencies, specifically blocks and prolongations, using acoustic features. Because no publicly available annotated resources exist for this task in Brazilian Portuguese, we built a initial dataset annotated by specialists. The system achieved 89.5% accuracy in classifying complex disfluencies, with a Matthews Correlation Coeficient (MCC) of 0.812. These results indicate the feasibility of the tool as decision support for clinical assessment and establish a baseline for future research.
%U https://aclanthology.org/2026.propor-1.50/
%P 507-515

Markdown (Informal)

[Detecting Stuttering with Artificial Intelligence: A Hybrid Method for Brazilian Portuguese](https://aclanthology.org/2026.propor-1.50/) (Buzzeti et al., PROPOR 2026)

• Detecting Stuttering with Artificial Intelligence: A Hybrid Method for Brazilian Portuguese (Buzzeti et al., PROPOR 2026)

ACL

• Rubens Perini Buzzeti, Paula Bianca Meireles de Moura Buzzeti, and Roney Lira de Sales Santos. 2026. Detecting Stuttering with Artificial Intelligence: A Hybrid Method for Brazilian Portuguese. In Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1, pages 507–515, Salvador, Brazil. Association for Computational Linguistics.