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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)
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)
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.
#4 best model for Speech Recognition on LibriSpeech test-other
The performance of automatic speech recognition (ASR) has improved tremendously due to the application of deep neural networks (DNNs).
Recently, there has been an increasing interest in end-to-end speech recognition that directly transcribes speech to text without any predefined alignments.
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.
#9 best model for Speech Recognition on LibriSpeech test-clean