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In this work, we explore a Connectionist Temporal Classification (CTC) basedend-to-end Automatic Speech Recognition (ASR) model for the Myanmar language. Aseries of experiments is presented on the topology of the model in which theconvolutional layers are added and dropped, different depths of bidirectionallong short-term memory (BLSTM) layers are used and different label encodingmethods are investigated. The experiments are carried out in low-resourcescenarios using our recorded Myanmar speech corpus of nearly 26 hours. The bestmodel achieves character error rate (CER) of 4.72% and syllable error rate(SER) of 12.38% on the test set.

Exploring CTC Based End-to-End Techniques for Myanmar Speech Recognition