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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
Unsupervised Automatic Speech Recognition: A Review—0
Unsupervised Cross-Domain Singing Voice Conversion—0
Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces—0
Unsupervised data selection for Speech Recognition with contrastive loss ratios—0
Unsupervised Data Selection via Discrete Speech Representation for ASR—0
Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation—0
Unsupervised Domain Adaptation for Speech Recognition via Uncertainty Driven Self-Training—0
Unsupervised domain adaptation for speech recognition with unsupervised error correction—0
Unsupervised Domain Adaptation in Speech Recognition using Phonetic Features—0
Unsupervised Domain Adaptation Schemes for Building ASR in Low-resource Languages—0
Unsupervised Domain Discovery using Latent Dirichlet Allocation for Acoustic Modelling in Speech Recognition—0
Unsupervised Fine-Tuning Data Selection for ASR Using Self-Supervised Speech Models—0
Unsupervised Method for Improving Arabic Speech Recognition Systems—0
Unsupervised Model-based speaker adaptation of end-to-end lattice-free MMI model for speech recognition—0
Unsupervised morph segmentation and statistical language models for vocabulary expansion—0
Unsupervised Pattern Discovery from Thematic Speech Archives Based on Multilingual Bottleneck Features—0
Unsupervised pre-training for sequence to sequence speech recognition—0
Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR—0
Unsupervised Speaker Adaptation using Attention-based Speaker Memory for End-to-End ASR—0
Unsupervised Speech Enhancement Based on Multichannel NMF-Informed Beamforming for Noise-Robust Automatic Speech Recognition—0
Unsupervised Stemming based Language Model for Telugu Broadcast News Transcription—0
Unveiling Biases while Embracing Sustainability: Assessing the Dual Challenges of Automatic Speech Recognition Systems—0
Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End ASR Models—0
Useful Blunders: Can Automated Speech Recognition Errors Improve Downstream Dementia Classification?—0
Use of Knowledge Graph in Rescoring the N-Best List in Automatic 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