Papers by Sandra Aluísio
MuPe Life Stories Dataset: Spontaneous Speech in Brazilian Portuguese with a Case Study Evaluation on ASR Bias against Speakers Groups and Topic Modeling (2025.coling-main)
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Sidney Evaldo Leal, Arnaldo Candido Junior, Ricardo Marcacini, Edresson Casanova, Odilon Gonçalves, Anderson Silva Soares, Rodrigo Freitas Lima, Lucas Rafael Stefanel Gris, Sandra Aluísio
| Challenge: | Recent datasets for automatic speech recognition in Brazilian Portuguese lack diversity in terms of age groups, regional accents, and education levels. |
| Approach: | They propose to use a dataset to analyze the impact of ASR in Brazilian Portuguese (BP) they demonstrate that current models are biased regarding age, education, and regional accents. |
| Outcome: | The proposed dataset helps mitigate biases in current ASR models regarding education levels and age groups. |
Evaluating Sentence Segmentation in Different Datasets of Neuropsychological Language Tests in Brazilian Portuguese (2020.lrec-1)
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| Challenge: | Using automated analysis of connected speech is a promising direction for diagnosing cognitive impairments. |
| Approach: | They propose to use a novel model to segment impaired speech transcriptions . they propose to include a Linear Chain CRF and a self-attention mechanism . |
| Outcome: | The proposed system performs better than the existing model with three new datasets used to diagnose cognitive impairments. |
Using Eye-tracking Data to Predict the Readability of Brazilian Portuguese Sentences in Single-task, Multi-task and Sequential Transfer Learning Approaches (2020.coling-main)
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Sidney Evaldo Leal, João Marcos Munguba Vieira, Erica dos Santos Rodrigues, Elisângela Nogueira Teixeira, Sandra Aluísio
| Challenge: | Sentence complexity assessment is a relatively new task in Natural Language Processing. |
| Approach: | They propose to use Brazilian Portuguese to evaluate sentences with linguistic features to improve readability. |
| Outcome: | The proposed model reaches the state-of-the-art for Brazilian Portuguese with 97.8% accuracy with linguistic features. |