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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 1801–1825 of 3012 papers

TitleStatusHype
VAD-free Streaming Hybrid CTC/Attention ASR for Unsegmented Recording—0
Zero-shot Speech Translation—0
The IWSLT 2021 BUT Speech Translation Systems—0
A Configurable Multilingual Model is All You Need to Recognize All Languages—0
Perceptual-based deep-learning denoiser as a defense against adversarial attacks on ASR systems—0
Noisy Training Improves E2E ASR for the Edge—0
On lattice-free boosted MMI training of HMM and CTC-based full-context ASR models—0
Loss Prediction: End-to-End Active Learning Approach For Speech Recognition—0
Improved Language Identification Through Cross-Lingual Self-Supervised Learning—0
End-to-End Rich Transcription-Style Automatic Speech Recognition with Semi-Supervised Learning—0
Advancing CTC-CRF Based End-to-End Speech Recognition with Wordpieces and Conformers—0
A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audio—0
Instant One-Shot Word-Learning for Context-Specific Neural Sequence-to-Sequence Speech RecognitionCode0
Investigation of Practical Aspects of Single Channel Speech Separation for ASR—0
Cross-Modal Transformer-Based Neural Correction Models for Automatic Speech Recognition—0
Unified Autoregressive Modeling for Joint End-to-End Multi-Talker Overlapped Speech Recognition and Speaker Attribute Estimation—0
Arabic Code-Switching Speech Recognition using Monolingual Data—0
Dual Causal/Non-Causal Self-Attention for Streaming End-to-End Speech Recognition—0
Multi-user VoiceFilter-Lite via Attentive Speaker Embedding—0
StableEmit: Selection Probability Discount for Reducing Emission Latency of Streaming Monotonic Attention ASR—0
Word-Free Spoken Language Understanding for Mandarin-Chinese—0
Improving Named Entity Recognition in Spoken Dialog Systems by Context and Speech Pattern Modeling—0
SmarTerp: A CAI System to Support Simultaneous Interpreters in Real-Time—0
Pretext Tasks selection for multitask self-supervised speech representation learningCode0
IMS' Systems for the IWSLT 2021 Low-Resource Speech Translation Task—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