SOTAVerified

Automatic Speech Recognition (ASR)

Automatic Speech Recognition (ASR) involves converting spoken language into written text. It is designed to transcribe spoken words into text in real-time, allowing people to communicate with computers, mobile devices, and other technology using their voice. The goal of Automatic Speech Recognition is to accurately transcribe speech, taking into account variations in accent, pronunciation, and speaking style, as well as background noise and other factors that can affect speech quality.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1826–1850 of 3012 papers

TitleStatusHype
Conditional independence for pretext task selection in Self-supervised speech representation learningCode0
Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation—0
Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept—0
Bridging the Gap Between Clean Data Training and Real-World Inference for Spoken Language Understanding—0
Improved Conformer-based End-to-End Speech Recognition Using Neural Architecture Search—0
Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures—0
Innovative Bert-based Reranking Language Models for Speech Recognition—0
NeMo Inverse Text Normalization: From Development To ProductionCode0
A Toolbox for Construction and Analysis of Speech DatasetsCode1
Non-autoregressive Transformer-based End-to-end ASR using BERT—0
Accented Speech Recognition Inspired by Human Perception—0
On Architectures and Training for Raw Waveform Feature Extraction in ASR—0
BSTC: A Large-Scale Chinese-English Speech Translation Dataset—0
Exploring Machine Speech Chain for Domain Adaptation and Few-Shot Speaker Adaptation—0
Contextual Semi-Supervised Learning: An Approach To Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems—0
RNN Transducer Models For Spoken Language UnderstandingCode1
WNARS: WFST based Non-autoregressive Streaming End-to-End Speech Recognition—0
Speak or Chat with Me: End-to-End Spoken Language Understanding System with Flexible InputsCode1
Pushing the Limits of Non-Autoregressive Speech Recognition—0
Capturing Multi-Resolution Context by Dilated Self-Attention—0
Exploring Targeted Universal Adversarial Perturbations to End-to-end ASR Models—0
Relaxing the Conditional Independence Assumption of CTC-based ASR by Conditioning on Intermediate Predictions—0
Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model—0
Dissecting User-Perceived Latency of On-Device E2E Speech Recognition—0
LT-LM: a novel non-autoregressive language model for single-shot lattice rescoringCode0
Show:102550
← PrevPage 74 of 121Next →
All datasetsLRS2RealMANSagaleeHUI speech corpusLRS3-TEDM-AILabs speech datasetThe Spoken Wikipedia CorporaVoxforge GermanVoxPopuli

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TM-CTCTest WER10.1—Unverified
2TM-seq2seqTest WER9.7—Unverified
3CTC/attentionTest WER8.2—Unverified
4LF-MMI TDNNTest WER6.7—Unverified
5Whisper-LLaMATest WER6.6—Unverified
6End2end ConformerTest WER3.9—Unverified
7End2end ConformerTest WER3.7—Unverified
8MoCo + wav2vec (w/o extLM)Test WER2.7—Unverified
9CTC/AttentionTest WER1.5—Unverified
10WhisperTest WER1.3—Unverified
#ModelMetricClaimedVerifiedStatus
1SpatialNetCER14.5—Unverified
2CleanMel-L-maskCER14.4—Unverified
#ModelMetricClaimedVerifiedStatus
1ConformerTest WER15.32—Unverified
2Whisper-largev3-finetunedTest WER10.82—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)1.89—Unverified
#ModelMetricClaimedVerifiedStatus
1DistillAVWER1.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)4.28—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)8.04—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)3.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer Transducer (German)WER (%)8.98—Unverified