Speech Recognition
Speech recognition is the task of recognising speech within audio and converting it into text.
( Image credit: SpecAugment )
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
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.
#3 best model 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
vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations
• pytorch/fairseq
• 
We propose vq-wav2vec to learn discrete representations of audio segments through a wav2vec-style self-supervised context prediction task.
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.
Semi-Supervised Speech Recognition via Local Prior Matching
For sequence transduction tasks like speech recognition, a strong structured prior model encodes rich information about the target space, implicitly ruling out invalid sequences by assigning them low probability.
#10 best model for
Speech Recognition
on LibriSpeech test-other
wav2letter++: The Fastest Open-source Speech Recognition System
This paper introduces wav2letter++, the fastest open-source deep learning speech recognition framework.
First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs
This approach to decoding enables first-pass speech recognition with a language model, completely unaided by the cumbersome infrastructure of HMM-based systems.
LANGUAGE MODELLING LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION SPEECH RECOGNITION
Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces
This paper presents the machine learning architecture of the Snips Voice Platform, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices.
#11 best model for
Speech Recognition
on LibriSpeech test-other
ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit
• espnet/espnet
•
Furthermore, the unified design enables the integration of ASR functions with TTS, e. g., ASR-based objective evaluation and semi-supervised learning with both ASR and TTS models.