Speech Recognition
503 papers with code • 103 benchmarks • 58 datasets
Speech recognition is the task of recognising speech within audio and converting it into text.
( Image credit: SpecAugment )
Benchmarks
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Datasets
MNIST
LibriSpeech
Speech Commands
MuST-C
AISHELL-1
Common Voice
CHiME-5
Subtasks
Greatest papers with code
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
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
Accented Speech Recognition
End-To-End Speech Recognition
+1
fairseq S2T: Fast Speech-to-Text Modeling with fairseq
huggingface/transformers
•
•
Asian Chapter of the Association for Computational Linguistics 2020
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 #8 on
Speech-to-Text Translation
on MuST-C EN->DE
Unsupervised Cross-lingual Representation Learning for Speech Recognition
This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages.
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
huggingface/transformers
•
•
NeurIPS 2020
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-clean
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
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
Deep Speech: Scaling up end-to-end speech recognition
We present a state-of-the-art speech recognition system developed using end-to-end deep learning.
Unsupervised Speech Recognition
pytorch/fairseq
•
•
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.
vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations
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)
wav2vec: Unsupervised Pre-training for Speech Recognition
pytorch/fairseq
•
•
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 Pre-training are Complementary for Speech Recognition
pytorch/fairseq
•
•
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)