Speech recognition is the task of recognising speech within audio and converting it into text.
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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.
Ranked #1 on Noisy Speech Recognition on CHiME clean
We present a state-of-the-art speech recognition system developed using end-to-end deep learning.
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.
Ranked #2 on Speech Recognition on Hub5'00 SwitchBoard (SwitchBoard metric)
We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler.
Ranked #1 on Speech Recognition on TIMIT (using extra training data)
We propose vq-wav2vec to learn discrete representations of audio segments through a wav2vec-style self-supervised context prediction task.
Ranked #2 on Speech Recognition on TIMIT (using extra training data)
Our experiments on WSJ reduce WER of a strong character-based log-mel filterbank baseline by up to 36% when only a few hours of transcribed data is available.
Ranked #5 on Speech Recognition on TIMIT (using extra training data)
We introduce fairseq S2T, a fairseq extension for speech-to-text (S2T) modeling tasks such as end-to-end speech recognition and speech-to-text translation.
For sequence transduction tasks like speech recognition, a strong structured prior model encodes rich information about the target space, implicitly ruling out invalid sequences by assigning them low probability.
Ranked #27 on Speech Recognition on LibriSpeech test-other
This paper introduces wav2letter++, the fastest open-source deep learning speech recognition framework.
We study pseudo-labeling for the semi-supervised training of ResNet, Time-Depth Separable ConvNets, and Transformers for speech recognition, with either CTC or Seq2Seq loss functions.
Ranked #7 on Speech Recognition on LibriSpeech test-clean