• Corpus ID: 238419427

Integrating Categorical Features in End-to-End ASR

@article{Huang2021IntegratingCF,
  title={Integrating Categorical Features in End-to-End ASR},
  author={Rongqing Huang},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.03047}
}
All-neural, end-to-end ASR systems gained rapid interest from the speech recognition community. Such systems convert speech input to text units using a single trainable neural network model. E2E models require large amounts of paired speech text data that is expensive to obtain. The amount of data available varies across different languages and dialects. It is critical to make use of all these data so that both low resource languages and high resource languages can be improved. When we want to… 

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