Papers by Edresson Casanova
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. |
Koel-TTS: Enhancing LLM based Speech Generation with Preference Alignment and Classifier Free Guidance (2025.emnlp-main)
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Shehzeen Samarah Hussain, Paarth Neekhara, Xuesong Yang, Edresson Casanova, Subhankar Ghosh, Roy Fejgin, Mikyas T. Desta, Rafael Valle, Jason Li
| Challenge: | Autoregressive speech token generation models suffer from hallucinations and undesired vocalizations that do not conform to conditioning inputs. |
| Approach: | They propose an encoder-decoder transformer model that improves contextual adherence of speech token generation LLMs through preference alignment and classifier-free guidance. |
| Outcome: | The proposed model outperforms previous LLM-based models on intelligibility, speaker similarity and naturalness. |
Deep Learning against COVID-19: Respiratory Insufficiency Detection in Brazilian Portuguese Speech (2021.findings-acl)
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Edresson Casanova, Lucas Gris, Augusto Camargo, Daniel da Silva, Murilo Gazzola, Ester Sabino, Anna Levin, Arnaldo Candido Jr, Sandra Aluisio, Marcelo Finger
| Challenge: | Respiratory insufficiency is a symptom that requires hospitalization . a dataset was created to analyze COVID-19 patients and a control group . |
| Approach: | They used a dataset to build a Convolution Neural Network to detect respiratory insufficiency using MFCC representations. |
| Outcome: | The proposed method achieves 91.66% accuracy under real-life environmental conditions. |