Arquivos para teste (Test files) Separadores: Tabulado, virgula, ponto e virgula (tabular, comma, dot and comma)

Arquivos para teste (Test files) Separadores: Tabulado, virgula, ponto e virgula (tabular, comma, dot and comma)

This repository contains all the pretraining datasets used in the paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers by Marcelo Gauy and Marcelo Finger. These datasets are part of a collection of datasets from the TaRSila project (see https://sites.google.com/view/tarsila-c4ai). The audios published here were in part also published with annotations and transcriptions as the CORAA dataset (see https://github.com/nilc-nlp/CORAA). Here we publish the original raw audios from the following datasets (without transcriptions) - ALIP, C-Oral, SP2010, NURC-Recife, NURC-São Paulo and Programa Certas Palavras. In total, the datasets contain about 800 hours of Brazilian Portuguese Speech. The audios have been converted to mp3 to facilitate the upload. ALIP, C-Oral and SP2010 are integrally contained in one file each. Programa Certas Palavras and NURC-Recife are split in 3 parts each, while NURC-SP is split in 7 parts of roughly equal size. More information on the datasets can be found in the paper Acoustic models of Brazilian Portuguese Speech based on Neural Transformers as well as on the original references which created these datasets.

This repository contains all the pretraining datasets used in the paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers by Marcelo Gauy and Marcelo Finger. These datasets are part of a collection of datasets from the TaRSila project (see https://sites.google.com/view/tarsila-c4ai). The audios published here were in part also published with annotations and transcriptions as the CORAA dataset (see https://github.com/nilc-nlp/CORAA). Here we publish the original raw audios from the following datasets (without transcriptions) - ALIP, C-Oral, SP2010, NURC-Recife, NURC-São Paulo and Programa Certas Palavras. In total, the datasets contain about 800 hours of Brazilian Portuguese Speech. The audios have been converted to mp3 to facilitate the upload. ALIP, C-Oral and SP2010 are integrally contained in one file each. Programa Certas Palavras and NURC-Recife are split in 3 parts each, while NURC-SP is split in 7 parts of roughly equal size. More information on the datasets can be found in the paper Acoustic models of Brazilian Portuguese Speech based on Neural Transformers as well as on the original references which created these datasets.

This dataset was collected over the internet and in hospital wards with the goal of detecting respiratory insufficiency (typically caused by COVID-19). This data collection is part of the SPIRA Project, whose goal is developing a system for recognizing respiratory insufficiency through speech analysis. The datasets presented here were used in the paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers by Marcelo Gauy and Marcelo Finger. The spira_trimmed_data file contains the original ~1 hour dataset collected over the internet (control) and in hospital wards (patients) by the SPIRA Project. This is as described in the paper: Deep learning against COVID-19: Respiratory insufficiency detection in Brazilian Portuguese Speech. We include it here for completeness. The spira_control_full_mp3 file contains the complete ~18 hours control data collected over the internet by the SPIRA project. While not useful for respiratory insufficiency detection, the dataset may be used for identifying age and gender as we mention in our paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers.

This dataset was collected over the internet and in hospital wards with the goal of detecting respiratory insufficiency (typically caused by COVID-19). This data collection is part of the SPIRA Project, whose goal is developing a system for recognizing respiratory insufficiency through speech analysis. The datasets presented here were used in the paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers by Marcelo Gauy and Marcelo Finger. The spira_trimmed_data file contains the original ~1 hour dataset collected over the internet (control) and in hospital wards (patients) by the SPIRA Project. This is as described in the paper: Deep learning against COVID-19: Respiratory insufficiency detection in Brazilian Portuguese Speech. We include it here for completeness. The spira_control_full_mp3 file contains the complete ~18 hours control data collected over the internet by the SPIRA project. While not useful for respiratory insufficiency detection, the dataset may be used for identifying age and gender as we mention in our paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers.

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A nota detalha os procedimentos metodológicos do artigo “Retórica da ameaça e securitização. A política migratória dos Estados Unidos na administração Trump”, principalmente o método da Análise Crítica do Discurso.

The proposal of this work was to estimate the genetic variability in orchards of Pinus caribaea var. caribaea based on growth traits and to analyze the best selection method. This study was conducted in two areas of P. caribaea var. caribaea situated in Savannah biome. The first orchard was a randomized complete block design with 76 progenies and 4 controls (area 1), the second orchard, the lattice design was 10x10 with 99 progenies and one control (area 2), 28 and 27 years old, respectively. The software SELEGEN was used to estimate genetic parameters trough REML/BLUP method. Significant variation was observed between and with progeny all traits in area 2 and only between plants within plots for height in area 1. The highest estimates of genetic variation and heritability were obtained for area 1. Without the optimization of selection, the highest gain (4.8%) in the selection between and within with a selection intensity of 52%, for area 1. In area 2, the highest gain (2.86%) in individual selection. We conclude that there is low genetic variability in seedlings orchards of P. caribaea var. caribaea. However, area 1 presents higher genetic control than area 2, and should be better explored. For the next generations, it is recommended the infusion of new genetic material to proceed with a forest improvement program, since it was observed low variability and low gains in the selection of P. caribaea var. caribaea.

The proposal of this work was to estimate the genetic variability in orchards of Pinus caribaea var. caribaea based on growth traits and to analyze the best selection method. This study was conducted in two areas of P. caribaea var. caribaea situated in Savannah biome. The first orchard was a randomized complete block design with 76 progenies and 4 controls (area 1), the second orchard, the lattice design was 10x10 with 99 progenies and one control (area 2), 28 and 27 years old, respectively. The software SELEGEN was used to estimate genetic parameters trough REML/BLUP method. Significant variation was observed between and with progeny all traits in area 2 and only between plants within plots for height in area 1. The highest estimates of genetic variation and heritability were obtained for area 1. Without the optimization of selection, the highest gain (4.8%) in the selection between and within with a selection intensity of 52%, for area 1. In area 2, the highest gain (2.86%) in individual selection. We conclude that there is low genetic variability in seedlings orchards of P. caribaea var. caribaea. However, area 1 presents higher genetic control than area 2, and should be better explored. For the next generations, it is recommended the infusion of new genetic material to proceed with a forest improvement program, since it was observed low variability and low gains in the selection of P. caribaea var. caribaea.