Speech Recognition
Speech recognition is the task of recognising speech within audio and converting it into text.
( Image credit: SpecAugment )
Leaderboards
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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.
SOTA for Speech Recognition on WSJ eval93 (using extra training data)
ACCENTED SPEECH RECOGNITION END-TO-END SPEECH RECOGNITION NOISY SPEECH RECOGNITION
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.
SOTA for Speech Recognition on LibriSpeech test-clean (using extra training data)
DATA AUGMENTATION END-TO-END SPEECH RECOGNITION LANGUAGE MODELLING SPEECH RECOGNITION
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.
#2 best model for
Noisy Speech Recognition
on CHiME clean
End-to-end speech recognition using lattice-free MMI
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.
Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks
Time Delay Neural Networks (TDNNs), also known as onedimensional Convolutional Neural Networks (1-d CNNs), are an efficient and well-performing neural network architecture for speech recognition.
Neural Network Language Modeling with Letter-based Features and Importance Sampling
In this paper we describe an extension of the Kaldi software toolkit to support neural-based language modeling, intended for use in automatic speech recognition (ASR) and related tasks.
#3 best model for
Speech Recognition
on LibriSpeech test-other
Building state-of-the-art distant speech recognition using the CHiME-4 challenge with a setup of speech enhancement baseline
This paper describes a new baseline system for automatic speech recognition (ASR) in the CHiME-4 challenge to promote the development of noisy ASR in speech processing communities by providing 1) state-of-the-art system with a simplified single system comparable to the complicated top systems in the challenge, 2) publicly available and reproducible recipe through the main repository in the Kaldi speech recognition toolkit.
DISTANT SPEECH RECOGNITION LANGUAGE MODELLING NOISY SPEECH RECOGNITION SPEECH ENHANCEMENT
Purely sequence-trained neural networks for ASR based on lattice-free MMI
Models trained with LFMMI provide a relative word error rate reduction of ∼11. 5%, over those trained with cross-entropy objective function, and ∼8%, over those trained with cross-entropy and sMBR objective functions.
SOTA for Speech Recognition on WSJ eval92
LANGUAGE MODELLING LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION SPEECH RECOGNITION
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.
Who Needs Words? Lexicon-Free Speech Recognition
Lexicon-free speech recognition naturally deals with the problem of out-of-vocabulary (OOV) words.