16 citations · 37 across the 12 of their papers we have counts for
12 papers
Fine-tuning Pre-trained Audio Models for COVID-19 Detection: A Technical Report
Daniel Oliveira de Brito, Letícia Gabriella de Souza, Marcelo Matheus Gauy +2
This technical report investigates the performance of pre-trained audio models on COVID-19 detection tasks using established benchmark datasets. We fine-tuned Audio-MAE and three P…
The Impact of Prosodic Segmentation on Speech Synthesis of Spontaneous Speech
Julio Cesar Galdino, Sidney Evaldo Leal, Leticia Gabriella De Souza +6
Spontaneous speech presents several challenges for speech synthesis, particularly in capturing the natural flow of conversation, including turn-taking, pauses, and disfluencies. Al…
Contrasting Deep Learning Models for Direct Respiratory Insufficiency Detection Versus Blood Oxygen Saturation Estimation
Marcelo Matheus Gauy, Natalia Hitomi Koza, Ricardo Mikio Morita +7
We contrast high effectiveness of state of the art deep learning architectures designed for general audio classification tasks, refined for respiratory insufficiency (RI) detection…
Introducing Bode: A Fine-Tuned Large Language Model for Portuguese Prompt-Based Task
Gabriel Lino Garcia, Pedro Henrique Paiola, Luis Henrique Morelli +7
Large Language Models (LLMs) are increasingly bringing advances to Natural Language Processing. However, low-resource languages, those lacking extensive prominence in datasets for…
CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages
Frederico S. Oliveira, Edresson Casanova, Arnaldo Cândido Júnior +2
In this paper, we present CML-TTS, a recursive acronym for CML-Multi-Lingual-TTS, a new Text-to-Speech (TTS) dataset developed at the Center of Excellence in Artificial Intelligenc…
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…
Evaluating OpenAI's Whisper ASR for Punctuation Prediction and Topic Modeling of life histories of the Museum of the Person
Lucas Rafael Stefanel Gris, Ricardo Marcacini, Arnaldo Candido Junior +3
Automatic speech recognition (ASR) systems play a key role in applications involving human-machine interactions. Despite their importance, ASR models for the Portuguese language pr…
Bringing NURC/SP to Digital Life: the Role of Open-source Automatic Speech Recognition Models
Lucas Rafael Stefanel Gris, Arnaldo Candido Junior, Vinícius G. dos Santos +4
The NURC Project that started in 1969 to study the cultured linguistic urban norm spoken in five Brazilian capitals, was responsible for compiling a large corpus for each capital.…
CORAA: a large corpus of spontaneous and prepared speech manually validated for speech recognition in Brazilian Portuguese
Arnaldo Candido Junior, Edresson Casanova, Anderson Soares +8
Automatic Speech recognition (ASR) is a complex and challenging task. In recent years, there have been significant advances in the area. In particular, for the Brazilian Portuguese…
Brazilian Portuguese Speech Recognition Using Wav2vec 2.0
Lucas Rafael Stefanel Gris, Edresson Casanova, Frederico Santos de Oliveira +2
Deep learning techniques have been shown to be efficient in various tasks, especially in the development of speech recognition systems, that is, systems that aim to transcribe an a…
TTS-Portuguese Corpus: a corpus for speech synthesis in Brazilian Portuguese
Edresson Casanova, Arnaldo Candido Junior, Christopher Shulby +4
Speech provides a natural way for human-computer interaction. In particular, speech synthesis systems are popular in different applications, such as personal assistants, GPS applic…
Speech2Phone: A Novel and Efficient Method for Training Speaker Recognition Models
Edresson Casanova, Arnaldo Candido Junior, Christopher Shulby +5
In this paper we present an efficient method for training models for speaker recognition using small or under-resourced datasets. This method requires less data than other SOTA (St…