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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 2976–3000 of 3012 papers

TitleStatusHype
Speech Recognition Web Services for Dutch—0
The Nijmegen Corpus of Casual Czech—0
ASR-based CALL systems and learner speech data: new resources and opportunities for research and development in second language learning—0
Automatic language identity tagging on word and sentence-level in multilingual text sources: a case-study on Luxembourgish—0
Free English and Czech telephone speech corpus shared under the CC-BY-SA 3.0 license—0
Euronews: a multilingual speech corpus for ASR—0
TUKE-BNews-SK: Slovak Broadcast News Corpus Construction and Evaluation—0
Student achievement and French sentence repetition test scores—0
The ETAPE speech processing evaluation—0
A Subband-Based SVM Front-End for Robust ASR—0
Speech Recognition Front End Without Information Loss—0
Error Detection in Automatic Speech Recognition—0
A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition—0
Local Feature or Mel Frequency Cepstral Coefficients - Which One is Better for MLN-Based Bangla Speech Recognition?—0
Improving Language Model Adaptation using Automatic Data Selection and Neural Network—0
Automating speech reception threshold measurements using automatic speech recognition—0
Automatic Speech Recognition: A Shifted Role in Early Speech Intervention?—0
Analyzing the Performance of Automatic Speech Recognition for Ageing Voice: Does it Correlate with Dependency Level?—0
Automatic speech recognition in the diagnosis of primary progressive aphasia—0
homeService: Voice-enabled assistive technology in the home using cloud-based automatic speech recognition—0
Opportunities & Challenges In Automatic Speech Recognition—0
Automatic Speech Recognition Using Template Model for Man-Machine Interface—0
Speech Enhancement Modeling Towards Robust Speech Recognition System—0
Analysis of Phonetic Transcription for Danish Automatic Speech Recognition—0
Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks—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