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We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech--two vastly different languages.
SOTA for Speech Recognition on WSJ eval93 (using extra training data)
We present a state-of-the-art speech recognition system developed using end-to-end deep learning.
#2 best model for Noisy Speech Recognition on CHiME clean
We present our work on end-to-end training of acoustic models using the lattice-free maximum mutual information (LF-MMI) objective function in the context of hidden Markov models.
In this paper, we report state-of-the-art results on LibriSpeech among end-to-end speech recognition models without any external training data.
#2 best model for Speech Recognition on LibriSpeech test-clean
To the best knowledge of the authors, the results obtained when training on the full LibriSpeech training set, are the best published currently, both for the hybrid DNN/HMM and the attention-based systems.
Sequence-to-sequence attention-based models on subword units allow simple open-vocabulary end-to-end speech recognition.
#7 best model for Speech Recognition on LibriSpeech test-clean
On LibriSpeech, we achieve 6. 8% WER on test-other without the use of a language model, and 5. 8% WER with shallow fusion with a language model.
SOTA for Speech Recognition on LibriSpeech test-clean (using extra training data)