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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 1926–1950 of 3012 papers

TitleStatusHype
Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model—0
Exploring Targeted Universal Adversarial Perturbations to End-to-end ASR Models—0
Flexi-Transducer: Optimizing Latency, Accuracy and Compute forMulti-Domain On-Device Scenarios—0
Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding—0
Speaker conditioned acoustic modeling for multi-speaker conversational ASR—0
Talk, Don't Write: A Study of Direct Speech-Based Image Retrieval—0
Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition—0
End-to-End Speaker-Attributed ASR with Transformer—0
Towards Lifelong Learning of End-to-end ASR—0
Adversarial Joint Training with Self-Attention Mechanism for Robust End-to-End Speech Recognition—0
On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASRCode0
Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yol\'oxochitl Mixtec—0
Context-sensitive evaluation of automatic speech recognition: considering user experience & language variation—0
Tutorial Proposal: End-to-End Speech Translation—0
Dialect Identification through Adversarial Learning and Knowledge Distillation on Romanian BERT—0
Configurable Privacy-Preserving Automatic Speech Recognition—0
Adversarial Attacks and Defenses for Speech Recognition Systems—0
Large-Scale Pre-Training of End-to-End Multi-Talker ASR for Meeting Transcription with Single Distant Microphone—0
Multi-Encoder Learning and Stream Fusion for Transformer-Based End-to-End Automatic Speech Recognition—0
Multiple-hypothesis CTC-based semi-supervised adaptation of end-to-end speech recognition—0
Quantifying Bias in Automatic Speech RecognitionCode0
Mutually-Constrained Monotonic Multihead Attention for Online ASR—0
Construction of a Large-scale Japanese ASR Corpus on TV Recordings—0
BART based semantic correction for Mandarin automatic speech recognition system—0
Residual Energy-Based Models for End-to-End Speech Recognition—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