activity
20232026
most citedEvaluation of Speech Representations for MOS prediction

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.AI2026

What Makes a Latent Space Shared? Direct and Recoverable Compatibility in Motion VAEs

Iago Brito, Walcy Rios, Lucas Fernandes +2

A shared latent space is often assumed to make representations from different schemas directly interchangeable, yet sharing parameters, dimensionality, or a prior does not determin…

cs.CV2026

SpatialChain: A Benchmark for Auditing Spatial Reasoning Faithfulness in VLMs

Rafael Teixeira Sousa, Vinícius Paulo Lopes de Oliveira, Elisa Ayumi Masasi de Oliveira +5

Thinking-enabled vision-language models (VLMs) report ever-higher accuracy on spatial benchmarks, yet final-answer scores cannot reveal whether a correct prediction reflects faithf…

cs.CL2026

Safety Is Not Universal: The Selective Safety Trap in LLM Alignment

Iago Alves Brito, Walcy Santos Rezende Rios, Julia Soares Dollis +2

Current safety evaluations of large language models (LLMs) create a dangerous illusion of universal protection by aggregating harms under generic categories such as "Identity Hate"…

cs.HC2025

When Avatars Have Personality: Effects on Engagement and Communication in Immersive Medical Training

Julia S. Dollis, Iago A. Brito, Fernanda B. Färber +5

While virtual reality (VR) excels at simulating physical environments, its effectiveness for training complex interpersonal skills is limited by a lack of psychologically plausible…

cs.CL2025

ToxSyn-PT: A Synthetic Fine-Grained Dataset of Minority-Targeted Toxic Language in Portuguese

Iago Alves Brito, Julia Soares Dollis, Fernanda Bufon Farber +2

The development of robust hate speech detection systems remains limited by the lack of large-scale, fine-grained training data, especially for languages beyond English. Existing co…

cs.SD2023★ 1 cited

Evaluation of Speech Representations for MOS prediction

Frederico S. Oliveira, Edresson Casanova, Arnaldo Cândido Júnior +3

In this paper, we evaluate feature extraction models for predicting speech quality. We also propose a model architecture to compare embeddings of supervised learning and self-super…