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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 2126–2150 of 3012 papers

TitleStatusHype
Text Injection for Neural Contextual Biasing—0
Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis—0
Text-only domain adaptation for end-to-end ASR using integrated text-to-mel-spectrogram generator—0
Text-Only Domain Adaptation for End-to-End Speech Recognition through Down-Sampling Acoustic Representation—0
Text-To-Speech Data Augmentation for Low Resource Speech Recognition—0
Thank you for Attention: A survey on Attention-based Artificial Neural Networks for Automatic Speech Recognition—0
That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages—0
The 2015 Sheffield System for Transcription of Multi-Genre Broadcast Media—0
The AFRL IWSLT 2018 Systems: What Worked, What Didn’t—0
The AFRL IWSLT 2020 Systems: Work-From-Home Edition—0
The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese—0
The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios—0
The CUHK-TENCENT speaker diarization system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge—0
The DIRHA Portuguese Corpus: A Comparison of Home Automation Command Detection and Recognition in Simulated and Real Data.—0
The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR—0
The Esethu Framework: Reimagining Sustainable Dataset Governance and Curation for Low-Resource Languages—0
The ETAPE speech processing evaluation—0
The evaluation of a code-switched Sepedi-English automatic speech recognition system—0
SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems—0
The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines—0
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning—0
The HW-TSC's Offline Speech Translation Systems for IWSLT 2021 Evaluation—0
The HW-TSC’s Offline Speech Translation System for IWSLT 2022 Evaluation—0
The IBM 2016 Speaker Recognition System—0
The IBM Speaker Recognition System: Recent Advances and Error Analysis—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