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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 1976–2000 of 3012 papers

TitleStatusHype
Attentively Embracing Noise for Robust Latent Representation in BERTCode0
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention—0
metaCAT: A Metadata-based Task-oriented Chatbot Annotation ToolCode1
Improving accuracy of rare words for RNN-Transducer through unigram shallow fusion—0
Transformer-Transducers for Code-Switched Speech Recognition—0
Unsupervised Domain Adaptation for Speech Recognition via Uncertainty Driven Self-Training—0
Bootstrap an end-to-end ASR system by multilingual training, transfer learning, text-to-text mapping and synthetic audio—0
Adam^+: A Stochastic Method with Adaptive Variance Reduction—0
Using Synthetic Audio to Improve The Recognition of Out-Of-Vocabulary Words in End-To-End ASR Systems—0
Multi-task Language Modeling for Improving Speech Recognition of Rare Words—0
Improving RNN-T ASR Accuracy Using Context Audio—0
WPD++: An Improved Neural Beamformer for Simultaneous Speech Separation and DereverberationCode0
Cascade RNN-Transducer: Syllable Based Streaming On-device Mandarin Speech Recognition with a Syllable-to-Character Converter—0
Refining Automatic Speech Recognition System for older adults—0
Audio-visual Multi-channel Integration and Recognition of Overlapped Speech—0
Deep Shallow Fusion for RNN-T Personalization—0
Improving Speech Enhancement Performance by Leveraging Contextual Broad Phonetic Class Information—0
Self-supervised reinforcement learning for speaker localisation with the iCub humanoid robot—0
Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through GradientsCode1
Simultaneous Speech-to-Speech Translation System with Neural Incremental ASR, MT, and TTS—0
Benchmarking LF-MMI, CTC and RNN-T Criteria for Streaming ASR—0
Gated Recurrent Fusion with Joint Training Framework for Robust End-to-End Speech Recognition—0
Personalized Query Rewriting in Conversational AI Agents—0
On the Usefulness of Self-Attention for Automatic Speech Recognition with Transformers—0
Stochastic Attention Head Removal: A simple and effective method for improving Transformer Based ASR ModelsCode0
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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