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Vietnamese Automatic Speech Recognition with Transformer

EasyChair Preprint no. 7147

4 pagesDate: December 4, 2021


Recently, speech recognition using end-toend models is gradually becoming a trend and has superior performance compared to traditional methods. The most frequently used methods are the combination of attention-based methods use an attention mechanism and connectionist temporal classification (CTC) for supervised Learning for Automatic Speech Recognition (ASR). In this paper, we propose a speech recognition model using the transformer architecture and achieved the top 3 in 2021 the Vietnamese Language and Speech Processing contest with 8.83% word error rate (WER) on private-test set.

Keyphrases: Attention Mechanism, end-to-end speech recognition, transformer

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Duong Trinh Anh and Sam Dang Van and Tuan Do Van and Vi Ngo Van},
  title = {Vietnamese Automatic Speech Recognition with Transformer},
  howpublished = {EasyChair Preprint no. 7147},

  year = {EasyChair, 2021}}
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