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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 676–700 of 3012 papers

TitleStatusHype
Echotune: A Modular Extractor Leveraging the Variable-Length Nature of Speech in ASR Tasks—0
EnCodecMAE: Leveraging neural codecs for universal audio representation learningCode1
Enhancing Child Vocalization Classification with Phonetically-Tuned Embeddings for Assisting Autism Diagnosis—0
Open-vocabulary Keyword-spotting with Adaptive Instance Normalization—0
Can Whisper perform speech-based in-context learning?—0
Improving Robustness of Neural Inverse Text Normalization via Data-Augmentation, Semi-Supervised Learning, and Post-Aligning Method—0
Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adults—0
Hybrid ASR for Resource-Constrained Robots: HMM - Deep Learning FusionCode0
Leveraging Large Language Models for Exploiting ASR Uncertainty—0
Perceptual and Task-Oriented Assessment of a Semantic Metric for ASR EvaluationCode0
Multiple Representation Transfer from Large Language Models to End-to-End ASR Systems—0
TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-device ASR Models—0
Bring the Noise: Introducing Noise Robustness to Pretrained Automatic Speech Recognition—0
AVATAR: Robust Voice Search Engine Leveraging Autoregressive Document Retrieval and Contrastive Learning—0
Text-Only Domain Adaptation for End-to-End Speech Recognition through Down-Sampling Acoustic Representation—0
Contextual Biasing of Named-Entities with Large Language Models—0
Learning Speech Representation From Contrastive Token-Acoustic Pretraining—0
Knowledge Distillation from Non-streaming to Streaming ASR Encoder using Auxiliary Non-streaming Layer—0
ASTER: Automatic Speech Recognition System Accessibility Testing for Stutterers—0
Unsupervised Active Learning: Optimizing Labeling Cost-Effectiveness for Automatic Speech Recognition—0
Decoupled Structure for Improved Adaptability of End-to-End Models—0
A Small and Fast BERT for Chinese Medical Punctuation RestorationCode0
Convoifilter: A case study of doing cocktail party speech recognition—0
TokenSplit: Using Discrete Speech Representations for Direct, Refined, and Transcript-Conditioned Speech Separation and Recognition—0
Indonesian Automatic Speech Recognition with XLSR-53—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