Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Aluisio, Sandra"'
We present a freely available spontaneous speech corpus for the Brazilian Portuguese language and report preliminary automatic speech recognition (ASR) results, using both the Wav2Vec2-XLSR-53 and Distil-Whisper models fine-tuned and trained on our c
Externí odkaz:
http://arxiv.org/abs/2409.15350
Autor:
Gris, Lucas Rafael Stefanel, Marcacini, Ricardo, Junior, Arnaldo Candido, Casanova, Edresson, Soares, Anderson, Aluísio, Sandra Maria
Automatic speech recognition (ASR) systems play a key role in applications involving human-machine interactions. Despite their importance, ASR models for the Portuguese language proposed in the last decade have limitations in relation to the correct
Externí odkaz:
http://arxiv.org/abs/2305.14580
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:
Gris, Lucas Rafael Stefanel, Junior, Arnaldo Candido, Santos, Vinícius G. dos, Dias, Bruno A. Papa, Leite, Marli Quadros, Svartman, Flaviane Romani Fernandes, Aluísio, Sandra
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. The digitized NURC/SP comprises 375 inquiries in 334 hours of recordin
Externí odkaz:
http://arxiv.org/abs/2210.07852
Autor:
Casanova, Edresson, Shulby, Christopher, Korolev, Alexander, Junior, Arnaldo Candido, Soares, Anderson da Silva, Aluísio, Sandra, Ponti, Moacir Antonelli
We explore cross-lingual multi-speaker speech synthesis and cross-lingual voice conversion applied to data augmentation for automatic speech recognition (ASR) systems in low/medium-resource scenarios. Through extensive experiments, we show that our a
Externí odkaz:
http://arxiv.org/abs/2204.00618
Autor:
Leal, Sidney Evaldo, Duran, Magali Sanches, Scarton, Carolina Evaristo, Hartmann, Nathan Siegle, Aluísio, Sandra Maria
This paper presents and makes publicly available the NILC-Metrix, a computational system comprising 200 metrics proposed in studies on discourse, psycholinguistics, cognitive and computational linguistics, to assess textual complexity in Brazilian Po
Externí odkaz:
http://arxiv.org/abs/2201.03445
Autor:
Junior, Arnaldo Candido, Casanova, Edresson, Soares, Anderson, de Oliveira, Frederico Santos, Oliveira, Lucas, Junior, Ricardo Corso Fernandes, da Silva, Daniel Peixoto Pinto, Fayet, Fernando Gorgulho, Carlotto, Bruno Baldissera, Gris, Lucas Rafael Stefanel, Aluísio, Sandra Maria
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 (BP) language, there were about 376 hours public available for ASR ta
Externí odkaz:
http://arxiv.org/abs/2110.15731
Autor:
Casanova, Edresson, Shulby, Christopher, Gölge, Eren, Müller, Nicolas Michael, de Oliveira, Frederico Santos, Junior, Arnaldo Candido, Soares, Anderson da Silva, Aluisio, Sandra Maria, Ponti, Moacir Antonelli
In this paper, we propose SC-GlowTTS: an efficient zero-shot multi-speaker text-to-speech model that improves similarity for speakers unseen during training. We propose a speaker-conditional architecture that explores a flow-based decoder that works
Externí odkaz:
http://arxiv.org/abs/2104.05557
Autor:
Casanova, Edresson, Junior, Arnaldo Candido, Shulby, Christopher, de Oliveira, Frederico Santos, Teixeira, João Paulo, Ponti, Moacir Antonelli, Aluisio, Sandra Maria
Speech provides a natural way for human-computer interaction. In particular, speech synthesis systems are popular in different applications, such as personal assistants, GPS applications, screen readers and accessibility tools. However, not all langu
Externí odkaz:
http://arxiv.org/abs/2005.05144
Autor:
Casanova, Edresson, Junior, Arnaldo Candido, Shulby, Christopher, de Oliveira, Frederico Santos, Gris, Lucas Rafael Stefanel, da Silva, Hamilton Pereira, Aluisio, Sandra Maria, Ponti, Moacir Antonelli
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 (State-Of-The-Art) methods, e.g. the Angular Prototypical and GE2E loss f
Externí odkaz:
http://arxiv.org/abs/2002.11213