In this work, we explore a Connectionist Temporal Classification (CTC) based end-to-end Automatic Speech Recognition (ASR) model for the Myanmar language. A series of experiments is presented on the topology of the model in which the convolutional layers are added and dropped, different depths of bidirectional long short-term memory (BLSTM) layers are used and different label encoding methods are investigated. The experiments are carried out in low-resource scenarios using our recorded Myanmar speech corpus of nearly 26 hours. The best model achieves character error rate (CER) of 4.72% and syllable error rate (SER) of 12.38% on the test set.
@article{arxiv.2105.06253,
title = {Exploring CTC Based End-to-End Techniques for Myanmar Speech Recognition},
author = {Khin Me Me Chit and Laet Laet Lin},
journal= {arXiv preprint arXiv:2105.06253},
year = {2021}
}
Comments
This is a preprint of the chapter: Chit K.M.M., Lin L.L., Exploring CTC Based End-To-End Techniques for Myanmar Speech Recognition, published in Advances in Intelligent Systems and Computing, vol 1324, edited by Vasant P., Zelinka I., Weber GW., 2021, Springer, Cham reproduced with permission of Springer. The final authenticated version is available at https://doi.org/10.1007/978-3-030-68154-8_87