Zobrazeno 1 - 10
of 174
pro vyhledávání: '"Finger, Marcelo"'
Autor:
Gauy, Marcelo Matheus, Koza, Natalia Hitomi, Morita, Ricardo Mikio, Stanzione, Gabriel Rocha, Junior, Arnaldo Candido, Berti, Larissa Cristina, Levin, Anna Sara Shafferman, Sabino, Ester Cerdeira, Svartman, Flaviane Romani Fernandes, Finger, Marcelo
We contrast high effectiveness of state of the art deep learning architectures designed for general audio classification tasks, refined for respiratory insufficiency (RI) detection and blood oxygen saturation (SpO$_2$) estimation and classification t
Externí odkaz:
http://arxiv.org/abs/2407.20989
Autor:
Gauy, Marcelo Matheus, Berti, Larissa Cristina, Cândido Jr, Arnaldo, Neto, Augusto Camargo, Goldman, Alfredo, Levin, Anna Sara Shafferman, Martins, Marcus, de Medeiros, Beatriz Raposo, Queiroz, Marcelo, Sabino, Ester Cerdeira, Svartman, Flaviane Romani Fernandes, Finger, Marcelo
Publikováno v:
Artificial Intellingence in Medicine Proceedings 2023, page 271-275
This work investigates Artificial Intelligence (AI) systems that detect respiratory insufficiency (RI) by analyzing speech audios, thus treating speech as a RI biomarker. Previous works collected RI data (P1) from COVID-19 patients during the first p
Externí odkaz:
http://arxiv.org/abs/2405.17569
Autor:
de Mello, Guilherme Lamartine, Finger, Marcelo, Serras, and Felipe, Carpi, Miguel de Mello, Jose, Marcos Menon, Domingues, Pedro Henrique, Cavalim, Paulo
In this paper we present PeLLE, a family of large language models based on the RoBERTa architecture, for Brazilian Portuguese, trained on curated, open data from the Carolina corpus. Aiming at reproducible results, we describe details of the pretrain
Externí odkaz:
http://arxiv.org/abs/2402.19204
Autor:
Gauy, Marcelo Matheus, Finger, Marcelo
An acoustic model, trained on a significant amount of unlabeled data, consists of a self-supervised learned speech representation useful for solving downstream tasks, perhaps after a fine-tuning of the model in the respective downstream task. In this
Externí odkaz:
http://arxiv.org/abs/2312.09265
Autor:
Crespo, Maria Clara Ramos Morales, Rocha, Maria Lina de Souza Jeannine, Sturzeneker, Mariana Lourenço, Serras, Felipe Ribas, de Mello, Guilherme Lamartine, Costa, Aline Silva, Palma, Mayara Feliciano, Mesquita, Renata Morais, Guets, Raquel de Paula, da Silva, Mariana Marques, Finger, Marcelo, de Sousa, Maria Clara Paixão, Namiuti, Cristiane, Monte, Vanessa Martins do
This paper presents the first publicly available version of the Carolina Corpus and discusses its future directions. Carolina is a large open corpus of Brazilian Portuguese texts under construction using web-as-corpus methodology enhanced with proven
Externí odkaz:
http://arxiv.org/abs/2303.16098
Autor:
da Silva, Daniel Peixoto Pinto, Casanova, Edresson, Gris, Lucas Rafael Stefanel, Junior, Arnaldo Candido, Finger, Marcelo, Svartman, Flaviane, Raposo, Beatriz, Martins, Marcus Vinícius Moreira, Aluísio, Sandra Maria, Berti, Larissa Cristina, Teixeira, João Paulo
During the outbreak of COVID-19 pandemic, several research areas joined efforts to mitigate the damages caused by SARS-CoV-2. In this paper we present an interpretability analysis of a convolutional neural network based model for COVID-19 detection i
Externí odkaz:
http://arxiv.org/abs/2211.14372
Autor:
Gauy, Marcelo Matheus, Finger, Marcelo
Publikováno v:
First Workshop on Automatic Speech Recognition for Spontaneous and Prepared Speech Speech emotion recognition in Portuguese (SER 2022)
The goal of speech emotion recognition (SER) is to identify the emotional aspects of speech. The SER challenge for Brazilian Portuguese speech was proposed with short snippets of Portuguese which are classified as neutral, non-neutral female and non-
Externí odkaz:
http://arxiv.org/abs/2210.14716
Autor:
Gauy, Marcelo Matheus, Finger, Marcelo
Publikováno v:
SIMP\'OSIO BRASILEIRO DE TECNOLOGIA DA INFORMA\c{C}\~AO E DA LINGUAGEM HUMANA (STIL), 13. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computa\c{c}\~ao, 2021 . p. 143-152
This work explores speech as a biomarker and investigates the detection of respiratory insufficiency (RI) by analyzing speech samples. Previous work \cite{spira2021} constructed a dataset of respiratory insufficiency COVID-19 patient utterances and a
Externí odkaz:
http://arxiv.org/abs/2210.14085
We introduce CoLN, Combined Learning of Neural network weights, a novel method to securely combine Machine Learning models over sensitive data with no sharing of data. With CoLN, local hosts use the same Neural Network architecture and base parameter
Externí odkaz:
http://arxiv.org/abs/2205.00361
Autor:
Serras, Felipe R., Finger, Marcelo
Publikováno v:
SERRAS, F. R.; FINGER, M. verBERT: Automating Brazilian Case Law Document Multi-label Categorization Using BERT. In 13th Brazilian Simposiun on Human Language and Information Technology (STIL), 2021. pp. 237-246
In this work, we carried out a study about the use of attention-based algorithms to automate the categorization of Brazilian case law documents. We used data from the Kollemata Project to produce two distinct datasets with adequate class systems. The
Externí odkaz:
http://arxiv.org/abs/2203.06224