Speech Recognition
1093 papers with code • 234 benchmarks • 87 datasets
Speech Recognition is the task of converting spoken language into text. It involves recognizing the words spoken in an audio recording and transcribing them into a written format. The goal is to accurately transcribe the speech in real-time or from recorded audio, taking into account factors such as accents, speaking speed, and background noise.
( 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
Libri-Light
AISHELL-1
LRS2
Subtasks
Most implemented papers
Wav2Letter: an End-to-End ConvNet-based Speech Recognition System
mailong25/vietnamese-speech-recognition
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arXiv 2016
This paper presents a simple end-to-end model for speech recognition, combining a convolutional network based acoustic model and a graph decoding.
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI.
Keyword Transformer: A Self-Attention Model for Keyword Spotting
ARM-software/keyword-transformer
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The Transformer architecture has been successful across many domains, including natural language processing, computer vision and speech recognition.
Efficiently Modeling Long Sequences with Structured State Spaces
hazyresearch/state-spaces
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ICLR 2022
A central goal of sequence modeling is designing a single principled model that can address sequence data across a range of modalities and tasks, particularly on long-range dependencies.
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
pytorch/fairseq
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Preprint 2022
While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind.
Robust Speech Recognition via Large-Scale Weak Supervision
openai/whisper
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Preprint 2022
We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet.
Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning
Recently, there has been an increasing interest in end-to-end speech recognition that directly transcribes speech to text without any predefined alignments.
wav2letter++: The Fastest Open-source Speech Recognition System
This paper introduces wav2letter++, the fastest open-source deep learning speech recognition framework.
HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training phase, and (3) sound units have variable lengths with no explicit segmentation.
Sequence Transduction with Recurrent Neural Networks
TensorSpeech/TensorFlowASR
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One of the key challenges in sequence transduction is learning to represent both the input and output sequences in a way that is invariant to sequential distortions such as shrinking, stretching and translating.
