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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 1076–1100 of 3012 papers

TitleStatusHype
A Comprehensive Study of the Current State-of-the-Art in Nepali Automatic Speech Recognition Systems—0
A Case Study on Combining ASR and Visual Features for Generating Instructional Video Captions—0
Bridging the gap between streaming and non-streaming ASR systems bydistilling ensembles of CTC and RNN-T models—0
Bridging the Gap Between Monaural Speech Enhancement and Recognition with Distortion-Independent Acoustic Modeling—0
``Oh, I've Heard That Before'': Modelling Own-Dialect Bias After Perceptual Learning by Weighting Training Data—0
Bridging the Gap Between Clean Data Training and Real-World Inference for Spoken Language Understanding—0
Bridging Speech and Textual Pre-trained Models with Unsupervised ASR—0
An Unsupervised Speaker Clustering Technique based on SOM and I-vectors for Speech Recognition Systems—0
Advancing Multi-talker ASR Performance with Large Language Models—0
ASR-enhanced Multimodal Representation Learning for Cross-Domain Product Retrieval—0
Error Detection in Automatic Speech Recognition—0
Étude de l'informativité des transcriptions : une approche basée sur le résumé automatique—0
Bridging Speech and Text: Enhancing ASR with Pinyin-to-Character Pre-training in LLMs—0
BridgeNets: Student-Teacher Transfer Learning Based on Recursive Neural Networks and its Application to Distant Speech Recognition—0
An Ultra-low Power RNN Classifier for Always-On Voice Wake-Up Detection Robust to Real-World Scenarios—0
Breaking Walls: Pioneering Automatic Speech Recognition for Central Kurdish: End-to-End Transformer Paradigm—0
Handling Trade-Offs in Speech Separation with Sparsely-Gated Mixture of Experts—0
Anti-spoofing Methods for Automatic SpeakerVerification System—0
Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy—0
Breaking the Transcription Bottleneck: Fine-tuning ASR Models for Extremely Low-Resource Fieldwork Languages—0
Breaking the Data Barrier: Towards Robust Speech Translation via Adversarial Stability Training—0
A Novel Self-training Approach for Low-resource Speech Recognition—0
Brain Signals to Rescue Aphasia, Apraxia and Dysarthria Speech Recognition—0
A Novel End-to-End CAPT System for L2 Children Learners—0
Advancing Hearing Assessment: An ASR-Based Frequency-Specific Speech Test for Diagnosing Presbycusis—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