Abstract
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 h. The best model achieves character error rate (CER) of 4.72% and syllable error rate (SER) of 12.38% on the test set.
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Acknowledgments
The authors are grateful to the advisors from the University of Information Technology who gave us helpful comments and suggestions throughout this project. The authors also thank Ye Yint Htoon and May Sabal Myo for helping us with the dataset preparation and for technical assistance.
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Chit, K.M.M., Lin, L.L. (2021). Exploring CTC Based End-To-End Techniques for Myanmar Speech Recognition. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_87
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