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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 1026–1050 of 3012 papers

TitleStatusHype
End-to-end Speech Recognition with Word-based RNN Language Models—0
End-to-end Speech-to-Punctuated-Text Recognition—0
End-to-End Spoken Grammatical Error Correction—0
Calibrate and Refine! A Novel and Agile Framework for ASR-error Robust Intent Detection—0
CAFE A Novel Code switching Dataset for Algerian Dialect French and English—0
Arabic Code-Switching Speech Recognition using Monolingual Data—0
Byte Pair Encoding Is All You Need For Automatic Bengali Speech Recognition—0
Bypass Temporal Classification: Weakly Supervised Automatic Speech Recognition with Imperfect Transcripts—0
Adversarial Joint Training with Self-Attention Mechanism for Robust End-to-End Speech Recognition—0
A Conformer-based ASR Frontend for Joint Acoustic Echo Cancellation, Speech Enhancement and Speech Separation—0
BUT System for the MLC-SLM Challenge—0
BUT Opensat 2019 Speech Recognition System—0
A Probabilistic Framework for Representing Dialog Systems and Entropy-Based Dialog Management through Dynamic Stochastic State Evolution—0
Building state-of-the-art distant speech recognition using the CHiME-4 challenge with a setup of speech enhancement baseline—0
Building Robust Spoken Language Understanding by Cross Attention between Phoneme Sequence and ASR Hypothesis—0
A privacy-preserving method using secret key for convolutional neural network-based speech classification—0
Adversarial Black-Box Attacks on Automatic Speech Recognition Systems using Multi-Objective Evolutionary Optimization—0
Accelerating Transducers through Adjacent Token Merging—0
Building Open-source Speech Technology for Low-resource Minority Languages with SáMi as an Example – Tools, Methods and Experiments—0
Building Open Javanese and Sundanese Corpora for Multilingual Text-to-Speech—0
A Preliminary Study on Automated Speaking Assessment of English as a Second Language (ESL) Students—0
Building English ASR model with regional language support—0
Building competitive direct acoustics-to-word models for English conversational speech recognition—0
A practical two-stage training strategy for multi-stream end-to-end speech recognition—0
Adversarial Attacks on ASR Systems: An Overview—0
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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