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

Showing 151175 of 3012 papers

TitleStatusHype
AVLnet: Learning Audio-Visual Language Representations from Instructional VideosCode1
AV Taris: Online Audio-Visual Speech RecognitionCode1
A context-aware knowledge transferring strategy for CTC-based ASRCode1
CL-MASR: A Continual Learning Benchmark for Multilingual ASRCode1
A Variance-Preserving Interpolation Approach for Diffusion Models with Applications to Single Channel Speech Enhancement and RecognitionCode1
AVATAR: Unconstrained Audiovisual Speech RecognitionCode1
Common Voice: A Massively-Multilingual Speech CorpusCode1
Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech RecognitionCode1
Back Translation for Speech-to-text Translation Without TranscriptsCode1
Continuous speech separation: dataset and analysisCode1
CopyNE: Better Contextual ASR by Copying Named EntitiesCode1
CORAA: a large corpus of spontaneous and prepared speech manually validated for speech recognition in Brazilian PortugueseCode1
Accented Speech Recognition With Accent-specific CodebooksCode1
CTC-synchronous Training for Monotonic Attention ModelCode1
Daily-Omni: Towards Audio-Visual Reasoning with Temporal Alignment across ModalitiesCode1
data2vec-aqc: Search for the right Teaching Assistant in the Teacher-Student training setupCode1
Deep Sparse Conformer for Speech RecognitionCode1
Can Contextual Biasing Remain Effective with Whisper and GPT-2?Code1
ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global ContextCode1
Automatic Severity Classification of Dysarthric speech by using Self-supervised Model with Multi-task LearningCode1
Distilling Knowledge from Ensembles of Acoustic Models for Joint CTC-Attention End-to-End Speech RecognitionCode1
Distilling the Knowledge of BERT for Sequence-to-Sequence ASRCode1
Automatic Disfluency Detection from Untranscribed SpeechCode1
Dompteur: Taming Audio Adversarial ExamplesCode1
Automatic Speech Recognition Benchmark for Air-Traffic CommunicationsCode1
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Benchmark Results

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