Corpus ID: 232380109

Residual Energy-Based Models for End-to-End Speech Recognition

@article{Li2021ResidualEM,
  title={Residual Energy-Based Models for End-to-End Speech Recognition},
  author={Qiujia Li and Yu Zhang and Bo Li and Liangliang Cao and P. Woodland},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.14152}
}
  • Qiujia Li, Yu Zhang, +2 authors P. Woodland
  • Published 2021
  • Computer Science, Engineering
  • ArXiv
End-to-end models with auto-regressive decoders have shown impressive results for automatic speech recognition (ASR). These models formulate the sequence-level probability as a product of the conditional probabilities of all individual tokens given their histories. However, the performance of locally normalised models can be sub-optimal because of factors such as exposure bias. Consequently, the model distribution differs from the underlying data distribution. In this paper, the residual energy… Expand
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