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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 1301–1325 of 3012 papers

TitleStatusHype
Arabic Language WEKA-Based Dialect Classifier for Arabic Automatic Speech Recognition Transcripts—0
Generative error correction for code-switching speech recognition using large language models—0
Fast and Accurate OOV Decoder on High-Level Features—0
Gesture-Aware Zero-Shot Speech Recognition for Patients with Language Disorders—0
Fast and Accurate Capitalization and Punctuation for Automatic Speech Recognition Using Transformer and Chunk Merging—0
Falling silent, lost for words ... Tracing personal involvement in interviews with Dutch war veterans—0
Calibration of Phone Likelihoods in Automatic Speech Recognition—0
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition—0
A Conformer-based Waveform-domain Neural Acoustic Echo Canceller Optimized for ASR Accuracy—0
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages—0
AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition—0
MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder—0
Fairness of Automatic Speech Recognition in Cleft Lip and Palate Speech—0
Graph based manifold regularized deep neural networks for automatic speech recognition—0
FairLENS: Assessing Fairness in Law Enforcement Speech Recognition—0
Calibrate and Refine! A Novel and Agile Framework for ASR-error Robust Intent Detection—0
Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation—0
Factual Consistency Oriented Speech Recognition—0
CAFE A Novel Code switching Dataset for Algerian Dialect French and English—0
Arabic Code-Switching Speech Recognition using Monolingual Data—0
Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation—0
Facetron: A Multi-speaker Face-to-Speech Model based on Cross-modal Latent Representations—0
Hallucination of speech recognition errors with sequence to sequence learning—0
Hallucinations in Neural Automatic Speech Recognition: Identifying Errors and Hallucinatory Models—0
Byte Pair Encoding Is All You Need For Automatic Bengali 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