Less is More: Improved RNN-T Decoding Using Limited Label Context and Path Merging

@article{Prabhavalkar2021LessIM,
  title={Less is More: Improved RNN-T Decoding Using Limited Label Context and Path Merging},
  author={Rohit Prabhavalkar and Yanzhang He and David Rybach and Sean Campbell and Arun Narayanan and Trevor Strohman and Tara N. Sainath},
  journal={ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2021},
  pages={5659-5663}
}
  • Rohit Prabhavalkar, Yanzhang He, +4 authors T. Sainath
  • Published 12 December 2020
  • Computer Science, Engineering
  • ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
End-to-end models that condition the output sequence on all previously predicted labels have emerged as popular alternatives to conventional systems for automatic speech recognition (ASR). Since distinct label histories correspond to distinct models states, such models are decoded using an approximate beam-search which produces a tree of hypotheses.In this work, we study the influence of the amount of label context on the model’s accuracy, and its impact on the efficiency of the decoding… Expand

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