478 papers with code • 104 benchmarks • 56 datasets
Speech recognition is the task of recognising speech within audio and converting it into text.
( Image credit: SpecAugment )
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 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.
Ranked #4 on Speech-to-Text Translation on MuST-C EN->DE
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 Libri-Light test-other
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 #1 on Speech Recognition on Hub5'00 SwitchBoard
We present a state-of-the-art speech recognition system developed using end-to-end deep learning.
Despite rapid progress in the recent past, current speech recognition systems still require labeled training data which limits this technology to a small fraction of the languages spoken around the globe.
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)
Self-training and unsupervised pre-training have emerged as effective approaches to improve speech recognition systems using unlabeled data.
Ranked #1 on Speech Recognition on LibriSpeech train-clean-100 test-clean (using extra training data)