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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

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Showing 2026–2050 of 3012 papers

TitleStatusHype
Speech Rate Calculations with Short Utterances: A Study from a Speech-to-Speech, Machine Translation Mediated Map Task—0
Speech Recognition by Simply Fine-tuning BERT—0
Speech recognition for air traffic control via feature learning and end-to-end training—0
Speech Recognition for Automatically Assessing Afrikaans and isiXhosa Preschool Oral Narratives—0
Speech recognition for medical conversations—0
Speech Recognition Front End Without Information Loss—0
Speech Recognition on TV Series with Video-guided Post-Correction—0
Speech Recognition Rescoring with Large Speech-Text Foundation Models—0
Speech Recognition Web Services for Dutch—0
Speech Recognition with no speech or with noisy speech—0
Speech recognition with quaternion neural networks—0
Speech Recognition with Quaternion Neural Networks—0
Speech Reconstruction from Silent Tongue and Lip Articulation By Pseudo Target Generation and Domain Adversarial Training—0
Speech Retrieval-Augmented Generation without Automatic Speech Recognition—0
Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English with Transfer Learning—0
Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English—0
Speech To Semantics: Improve ASR and NLU Jointly via All-Neural Interfaces—0
Speech-to-SQL Parsing: Error Correction with Multi-modal Representations—0
Speech-to-SQL: Towards Speech-driven SQL Query Generation From Natural Language Question—0
Speech-T: Transducer for Text to Speech and Beyond—0
Spelling Correction through Rewriting of Non-Autoregressive ASR Lattices—0
SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings—0
Spell my name: keyword boosted speech recognition—0
Spike-Triggered Contextual Biasing for End-to-End Mandarin Speech Recognition—0
SpliceOut: A Simple and Efficient Audio Augmentation Method—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