Speech Recognition
719 papers with code • 278 benchmarks • 184 datasets
Speech recognition is the task of recognising speech within audio and converting it into text.
( Image credit: SpecAugment )
Benchmarks
These leaderboards are used to track progress in Speech Recognition
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Libraries
Use these libraries to find Speech Recognition models and implementationsDatasets
MNIST
LibriSpeech
Speech Commands
Common Voice
MuST-C
AISHELL-1
Libri-Light
Subtasks
Most implemented papers
Listen, Attend and Spell
Alexander-H-Liu/End-to-end-ASR-Pytorch
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Unlike traditional DNN-HMM models, this model learns all the components of a speech recognizer jointly.
Neural Collaborative Filtering
microsoft/recommenders
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WWW 2017
When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items.
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.
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.
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
retrocirce/hts-audio-transformer
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Describes an audio dataset of spoken words designed to help train and evaluate keyword spotting systems.
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.
Recurrent Neural Network Regularization
We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units.
Conformer: Convolution-augmented Transformer for Speech Recognition
Recently Transformer and Convolution neural network (CNN) based models have shown promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (RNNs).
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
pytorch/fairseq
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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.
Improved training of end-to-end attention models for speech recognition
rwth-i6/returnn
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Sequence-to-sequence attention-based models on subword units allow simple open-vocabulary end-to-end speech recognition.
