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 5175 of 3012 papers

TitleStatusHype
wav2graph: A Framework for Supervised Learning Knowledge Graph from SpeechCode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface ElectromyographyCode2
Large Language Models are Efficient Learners of Noise-Robust Speech RecognitionCode2
Towards A Unified Conformer Structure: from ASR to ASV TaskCode2
Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy SpeechCode1
Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMICode1
ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control CommunicationsCode1
ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global ContextCode1
Continuous speech separation: dataset and analysisCode1
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural NetworksCode1
A transfer learning based approach for pronunciation scoringCode1
A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and EnglishCode1
A Survey on Non-Autoregressive Generation for Neural Machine Translation and BeyondCode1
Attention-based Audio-Visual Fusion for Robust Automatic Speech RecognitionCode1
ASR Error Correction with Constrained Decoding on Operation PredictionCode1
Framework for Curating Speech Datasets and Evaluating ASR Systems: A Case Study for PolishCode1
A Systematic Comparison of Phonetic Aware Techniques for Speech EnhancementCode1
Controlling Whisper: Universal Acoustic Adversarial Attacks to Control Speech Foundation ModelsCode1
A Sidecar Separator Can Convert a Single-Talker Speech Recognition System to a Multi-Talker OneCode1
ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversionCode1
Common Voice: A Massively-Multilingual Speech CorpusCode1
ArTST: Arabic Text and Speech TransformerCode1
ArzEn-LLM: Code-Switched Egyptian Arabic-English Translation and Speech Recognition Using LLMsCode1
Combining Frame-Synchronous and Label-Synchronous Systems for Speech RecognitionCode1
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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