An open-source end-to-end ASR system for Brazilian Portuguese using DNNs built from newly assembled corpora

By Igor M. Quintanilha, Sergio L. Netto, Luiz Wagner Pereira Biscainho. 2020

17 citations recorded by the source corpus.

In this work, we present a baseline end-to-end system based on deep learning for automatic speech recognition in Brazilian Portuguese. To build such a model, we employ a speech corpus containing 158 hours of annotated speech by assembling four individual datasets, three of them publicly available, and a text corpus containing 10.2 millions of sentences. We train an acoustic model based on the DeepSpeech 2 network, with two convolutional and five bidirectional recurrent layers. By adding a newly trained 15-gram language model at the character level, we achieve a character error rate of only 10.49% and a word error rate of 25.45%, which are on a par with other works in different languages using a similar amount of training data.

Subfields: Artificial Intelligence