Recurrent Neural Network Transducer for Audio-Visual Speech Recognition

@article{Makino2019RecurrentNN,
  title={Recurrent Neural Network Transducer for Audio-Visual Speech Recognition},
  author={T. Makino and H. Liao and Yannis M. Assael and Brendan Shillingford and Basi Garc{\'i}a and Otavio Braga and O. Siohan},
  journal={2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
  year={2019},
  pages={905-912}
}
  • T. Makino, H. Liao, +4 authors O. Siohan
  • Published 2019
  • Computer Science, Engineering
  • 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
This work presents a large-scale audio-visual speech recognition system based on a recurrent neural network transducer (RNN-T) architecture. To support the development of such a system, we built a large audio-visual (A/V) dataset of segmented utterances extracted from YouTube public videos, leading to 31k hours of audio-visual training content. The performance of an audio-only, visual-only, and audio-visual system are compared on two large-vocabulary test sets: a set of utterance segments from… Expand
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