Lucas Rafael Stefanel Gris — accepted AI-conference papers

2026

TAGARELA - A PORTUGUESE SPEECH DATASET FROM PODCASTS

Frederico Santos de Oliveira, Lucas Rafael Stefanel Gris, Augusto Seben da Rosa, Alexandre Costa Ferro Filho, Edresson Casanova, Christopher Dane Shulby +2

ICASSP 2026poster

Despite significant advances in speech processing, Portuguese remains under-resourced due to the scarcity of public, large-scale, and high-quality datasets. To address this gap, we present a new dataset, named TAGARELA, composed of over 8,972 hours of podcast audio, specifically curated for training…

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2025

FreeSVC: Towards Zero-shot Multilingual Singing Voice Conversion

Alef Iury Siqueira Ferreira, Lucas Rafael Stefanel Gris, Augusto Seben da Rosa, Frederico Santos de Oliveira, Edresson Casanova, Rafael Teixeira Sousa +3

ICASSP 2025accepted

This work presents FreeSVC, a promising multilingual singing voice conversion approach that leverages an enhanced VITS model with Speaker-invariant Clustering (SPIN) for better content representation and the State-of-the-Art (SOTA) speaker encoder ECAPA2. FreeSVC incorporates trainable language embe…

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2025

MuPe Life Stories Dataset: Spontaneous Speech in Brazilian Portuguese with a Case Study Evaluation on ASR Bias against Speakers Groups and Topic Modeling

Sidney Evaldo Leal, Arnaldo Candido Junior, Ricardo Marcacini, Edresson Casanova, Odilon Gonçalves, Anderson Silva Soares +3

COLING 2025main

Recently, several public datasets for automatic speech recognition (ASR) in Brazilian Portuguese (BP) have been released, improving ASR systems performance. However, these datasets lack diversity in terms of age groups, regional accents, and education levels. In this paper, we present a new publicly…

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