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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 751–775 of 3012 papers

TitleStatusHype
Predicting positive transfer for improved low-resource speech recognition using acoustic pseudo-tokens—0
Streaming Sequence Transduction through Dynamic CompressionCode0
AccentFold: A Journey through African Accents for Zero-Shot ASR Adaptation to Target Accents—0
Whispering in Norwegian: Navigating Orthographic and Dialectic Challenges—0
Digits micro-model for accurate and secure transactions—0
Byte Pair Encoding Is All You Need For Automatic Bengali Speech Recognition—0
Toward Practical Automatic Speech Recognition and Post-Processing: a Call for Explainable Error Benchmark Guideline—0
MF-AED-AEC: Speech Emotion Recognition by Leveraging Multimodal Fusion, Asr Error Detection, and Asr Error Correction—0
Locality enhanced dynamic biasing and sampling strategies for contextual ASR—0
Consistency Based Unsupervised Self-training For ASR Personalisation—0
Keep Decoding Parallel with Effective Knowledge Distillation from Language Models to End-to-end Speech Recognisers—0
Using Large Language Model for End-to-End Chinese ASR and NER—0
Contextualized Automatic Speech Recognition with Attention-Based Bias Phrase Boosted Beam Search—0
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks—0
AGADIR: Towards Array-Geometry Agnostic Directional Speech Recognition—0
SlideAVSR: A Dataset of Paper Explanation Videos for Audio-Visual Speech Recognition—0
Improving ASR Contextual Biasing with Guided Attention—0
Cascaded Cross-Modal Transformer for Audio-Textual ClassificationCode0
Promptformer: Prompted Conformer Transducer for ASR—0
Joint Unsupervised and Supervised Training for Automatic Speech Recognition via Bilevel Optimization—0
Transcending Controlled Environments Assessing the Transferability of ASRRobust NLU Models to Real-World Applications—0
XLS-R Deep Learning Model for Multilingual ASR on Low- Resource Languages: Indonesian, Javanese, and Sundanese—0
UCorrect: An Unsupervised Framework for Automatic Speech Recognition Error Correction—0
End to end Hindi to English speech conversion using Bark, mBART and a finetuned XLSR Wav2Vec2—0
Useful Blunders: Can Automated Speech Recognition Errors Improve Downstream Dementia Classification?—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