EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding

@article{Miao2015EESENES,
  title={EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding},
  author={Yajie Miao and Mohammad Gowayyed and Florian Metze},
  journal={2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)},
  year={2015},
  pages={167-174}
}
The performance of automatic speech recognition (ASR) has improved tremendously due to the application of deep neural networks (DNNs). Despite this progress, building a new ASR system remains a challenging task, requiring various resources, multiple training stages and significant expertise. This paper presents our Eesen framework which drastically… CONTINUE READING

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