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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 2026–2050 of 3012 papers

TitleStatusHype
Bayesian Learning of LF-MMI Trained Time Delay Neural Networks for Speech Recognition—0
MLS: A Large-Scale Multilingual Dataset for Speech ResearchCode0
Using multiple ASR hypotheses to boost i18n NLU performance—0
End to End ASR System with Automatic Punctuation InsertionCode0
On-Device detection of sentence completion for voice assistants with low-memory footprint—0
German-Arabic Speech-to-Speech Translation for Psychiatric Diagnosis—0
Sparse Transcription—0
ASR for Non-standardised Languages with Dialectal Variation: the case of Swiss German—0
A Comprehensive Evaluation of Incremental Speech Recognition and Diarization for Conversational AICode0
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention—0
The Indigenous Languages Technology project at NRC Canada: An empowerment-oriented approach to developing language software—0
Attentively Embracing Noise for Robust Latent Representation in BERTCode0
100,000 Podcasts: A Spoken English Document Corpus—0
Transformer-Transducers for Code-Switched Speech Recognition—0
Improving accuracy of rare words for RNN-Transducer through unigram shallow fusion—0
Unsupervised Domain Adaptation for Speech Recognition via Uncertainty Driven Self-Training—0
Bootstrap an end-to-end ASR system by multilingual training, transfer learning, text-to-text mapping and synthetic audio—0
Adam^+: A Stochastic Method with Adaptive Variance Reduction—0
Multi-task Language Modeling for Improving Speech Recognition of Rare Words—0
Using Synthetic Audio to Improve The Recognition of Out-Of-Vocabulary Words in End-To-End ASR Systems—0
Improving RNN-T ASR Accuracy Using Context Audio—0
WPD++: An Improved Neural Beamformer for Simultaneous Speech Separation and DereverberationCode0
Cascade RNN-Transducer: Syllable Based Streaming On-device Mandarin Speech Recognition with a Syllable-to-Character Converter—0
Refining Automatic Speech Recognition System for older adults—0
Audio-visual Multi-channel Integration and Recognition of Overlapped Speech—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