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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 1626–1650 of 3012 papers

TitleStatusHype
ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction—0
ASR Error Correction and Domain Adaptation Using Machine Translation—0
ASR Error Correction using Large Language Models—0
ASR Error Detection via Audio-Transcript entailment—0
ASR error management for improving spoken language understanding—0
ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems—0
ASR for Documenting Acutely Under-Resourced Indigenous Languages—0
ASR for Non-standardised Languages with Dialectal Variation: the case of Swiss German—0
ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language Understanding—0
ASR in German: A Detailed Error Analysis—0
ASR is all you need: cross-modal distillation for lip reading—0
ASR Rescoring and Confidence Estimation with ELECTRA—0
Assessing ASR Model Quality on Disordered Speech using BERTScore—0
Assessing the Performance of Automatic Speech Recognition Systems When Used by Native and Non-Native Speakers of Three Major Languages in Dictation Workflows—0
Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors—0
ASTER: Automatic Speech Recognition System Accessibility Testing for Stutterers—0
ASTRA: Aligning Speech and Text Representations for Asr without Sampling—0
A Study into Pre-training Strategies for Spoken Language Understanding on Dysarthric Speech—0
The Perceptimatic English Benchmark for Speech Perception Models—0
The RoyalFlush System of Speech Recognition for M2MeT Challenge—0
The SAFE-T Corpus: A New Resource for Simulated Public Safety Communications—0
The Second DISPLACE Challenge : DIarization of SPeaker and LAnguage in Conversational Environments—0
The SI TEDx-UM speech database: a new Slovenian Spoken Language Resource—0
The Slovene BNSI Broadcast News database and reference speech corpus GOS: Towards the uniform guidelines for future work—0
The Sogou-TIIC Speech Translation System for IWSLT 2018—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