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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 526–550 of 3012 papers

TitleStatusHype
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
Word-Level ASR Quality Estimation for Efficient Corpus Sampling and Post-Editing through Analyzing Attentions of a Reference-Free MetricCode1
Large Language Models are Efficient Learners of Noise-Robust Speech RecognitionCode2
Contextualized Automatic Speech Recognition with Attention-Based Bias Phrase Boosted Beam Search—0
SlideAVSR: A Dataset of Paper Explanation Videos for Audio-Visual Speech Recognition—0
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks—0
AGADIR: Towards Array-Geometry Agnostic Directional 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
Continuously Learning New Words in Automatic Speech Recognition—0
BS-PLCNet: Band-split Packet Loss Concealment Network with Multi-task Learning Framework and Multi-discriminators—0
LUPET: Incorporating Hierarchical Information Path into Multilingual ASR—0
High-precision Voice Search Query Correction via Retrievable Speech-text Embedings—0
Exploratory Evaluation of Speech Content Masking—0
Multichannel AV-wav2vec2: A Framework for Learning Multichannel Multi-Modal Speech RepresentationCode0
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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