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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 851–875 of 3012 papers

TitleStatusHype
Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition—0
Deep Recurrent Neural Networks for Acoustic Modelling—0
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends—0
Deep Shallow Fusion for RNN-T Personalization—0
Cascaded CNN-resBiLSTM-CTC: An End-to-End Acoustic Model For Speech Recognition—0
CarneliNet: Neural Mixture Model for Automatic Speech Recognition—0
Adversarial synthesis based data-augmentation for code-switched spoken language identification—0
Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization—0
Deep versus Wide: An Analysis of Student Architectures for Task-Agnostic Knowledge Distillation of Self-Supervised Speech Models—0
Deep Visual Forced Alignment: Learning to Align Transcription with Talking Face Video—0
Deep Xi as a Front-End for Robust Automatic Speech Recognition—0
Defense against Adversarial Attacks on Hybrid Speech Recognition using Joint Adversarial Fine-tuning with Denoiser—0
Dialect-Specific Models for Automatic Speech Recognition of African American Vernacular English—0
Deliberation Model Based Two-Pass End-to-End Speech Recognition—0
Deliberation Model for On-Device Spoken Language Understanding—0
Demystifying Limited Adversarial Transferability in Automatic Speech Recognition Systems—0
DENOASR: Debiasing ASRs through Selective Denoising—0
Denoising LM: Pushing the Limits of Error Correction Models for Speech Recognition—0
Densely Connected Convolutional Networks for Speech Recognition—0
Capturing Multi-Resolution Context by Dilated Self-Attention—0
Deploying self-supervised learning in the wild for hybrid automatic speech recognition—0
Deploying Technology to Save Endangered Languages—0
Design and Development of Speech Corpora for Air Traffic Control Training—0
Designing a Speech Corpus for the Development and Evaluation of Dictation Systems in Latvian—0
Capitalization and Punctuation Restoration: a Survey—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