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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 1051–1075 of 3012 papers

TitleStatusHype
Smart Speech Segmentation using Acousto-Linguistic Features with look-ahead—0
Monotonic segmental attention for automatic speech recognition—0
Linguistic-Enhanced Transformer with CTC Embedding for Speech Recognition—0
Time-Domain Speech Enhancement for Robust Automatic Speech Recognition—0
Does Joint Training Really Help Cascaded Speech Translation?Code0
Brouhaha: multi-task training for voice activity detection, speech-to-noise ratio, and C50 room acoustics estimationCode1
ESB: A Benchmark For Multi-Domain End-to-End Speech RecognitionCode1
Investigating self-supervised, weakly supervised and fully supervised training approaches for multi-domain automatic speech recognition: a study on Bangladeshi Bangla—0
Guided contrastive self-supervised pre-training for automatic speech recognition—0
Optimizing Bilingual Neural Transducer with Synthetic Code-switching Text Generation—0
Can Visual Context Improve Automatic Speech Recognition for an Embodied Agent?—0
Improving Semi-supervised End-to-end Automatic Speech Recognition using CycleGAN and Inter-domain Losses—0
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR—0
Discrete Cross-Modal Alignment Enables Zero-Shot Speech TranslationCode0
Sub-8-bit quantization for on-device speech recognition: a regularization-free approach—0
Continuous Pseudo-Labeling from the Start—0
Language-agnostic Code-Switching in Sequence-To-Sequence Speech Recognition—0
Towards Relation Extraction From SpeechCode1
LeVoice ASR Systems for the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge—0
Bringing NURC/SP to Digital Life: the Role of Open-source Automatic Speech Recognition ModelsCode0
Learning to Jointly Transcribe and Subtitle for End-to-End Spontaneous Speech Recognition—0
Experiments on Turkish ASR with Self-Supervised Speech Representation Learning—0
Can we use Common Voice to train a Multi-Speaker TTS system?Code1
Summary on the ISCSLP 2022 Chinese-English Code-Switching ASR Challenge—0
A context-aware knowledge transferring strategy for CTC-based ASRCode1
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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