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- W3196238820 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 hours. The best model achieves character error rate (CER) of 4.72% and syllable error rate (SER) of 12.38% on the test set." @default.
- W3196238820 created "2021-08-30" @default.
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- W3196238820 date "2021-01-01" @default.
- W3196238820 modified "2023-09-24" @default.
- W3196238820 title "Exploring CTC Based End-To-End Techniques for Myanmar Speech Recognition" @default.
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- W3196238820 doi "https://doi.org/10.1007/978-3-030-68154-8_87" @default.
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