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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 2326–2350 of 3012 papers

TitleStatusHype
Continuous Speech Recognition using EEG and Video—0
Synchronous Speech Recognition and Speech-to-Text Translation with Interactive DecodingCode0
Common Voice: A Massively-Multilingual Speech CorpusCode1
On Neural Phone Recognition of Mixed-Source ECoG Signals—0
Leveraging End-to-End Speech Recognition with Neural Architecture Search—0
SpecAugment on Large Scale Datasets—0
Audio-attention discriminative language model for ASR rescoring—0
Semantic Mask for Transformer based End-to-End Speech RecognitionCode0
Deep Contextualized Acoustic Representations For Semi-Supervised Speech RecognitionCode1
Improving Voice Separation by Incorporating End-to-end Speech RecognitionCode0
Kurdish (Sorani) Speech to Text: Presenting an Experimental DatasetCode0
Designing the Next Generation of Intelligent Personal Robotic Assistants for the Physically Impaired—0
ASR is all you need: cross-modal distillation for lip reading—0
ATCSpeech: a multilingual pilot-controller speech corpus from real Air Traffic Control environment—0
Independent language modeling architecture for end-to-end ASR—0
Improving EEG based Continuous Speech Recognition—0
Cantonese Automatic Speech Recognition Using Transfer Learning from Mandarin—0
On using 2D sequence-to-sequence models for speech recognition—0
CAT: CRF-based ASR ToolkitCode0
Deep Spiking Neural Networks for Large Vocabulary Automatic Speech RecognitionCode0
3-D Feature and Acoustic Modeling for Far-Field Speech Recognition—0
Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?—0
Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning—0
Listen and Fill in the Missing Letters: Non-Autoregressive Transformer for Speech Recognition—0
Evaluating Voice Conversion-based Privacy Protection against Informed Attackers—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