A Comparison of Transformer, Convolutional, and Recurrent Neural Networks on Phoneme Recognition

@article{Shim2022ACO,
  title={A Comparison of Transformer, Convolutional, and Recurrent Neural Networks on Phoneme Recognition},
  author={Kyuhong Shim and Wonyong Sung},
  journal={ArXiv},
  year={2022},
  volume={abs/2210.00367}
}
Phoneme recognition is a very important part of speech recognition that requires the ability to extract phonetic features from multiple frames. In this paper, we compare and analyze CNN, RNN, Transformer, and Conformer models using phoneme recognition. For CNN, the ContextNet model is used for the experiments. First, we compare the accuracy of various architectures under different constraints, such as the receptive field length, parameter size, and layer depth. Sec-ond, we interpret the… 

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