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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 1426–1450 of 3012 papers

TitleStatusHype
Improving Character Error Rate Is Not Equal to Having Clean Speech: Speech Enhancement for ASR Systems with Black-box Acoustic Models—0
Improving Code-switched ASR with Linguistic Information—0
Improving Code-Switching and Named Entity Recognition in ASR with Speech Editing based Data Augmentation—0
Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation—0
Improving Confidence Estimation on Out-of-Domain Data for End-to-End Speech Recognition—0
Improving Contextual Spelling Correction by External Acoustics Attention and Semantic Aware Data Augmentation—0
Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation—0
Improving CTC-AED model with integrated-CTC and auxiliary loss regularization—0
Improving CTC-based ASR Models with Gated Interlayer Collaboration—0
Construction of a Large-scale Japanese ASR Corpus on TV Recordings—0
Improving Data Driven Inverse Text Normalization using Data Augmentation—0
Improving Distinction between ASR Errors and Speech Disfluencies with Feature Space Interpolation—0
Improving Dysarthric Speech Intelligibility Using Cycle-consistent Adversarial Training—0
Improving EEG based Continuous Speech Recognition—0
Improving End-to-End Bangla Speech Recognition with Semi-supervised Training—0
Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis—0
Improving End-to-end Speech Recognition with Pronunciation-assisted Sub-word Modeling—0
Enhancing Lyrics Transcription on Music Mixtures with Consistency Loss—0
Improving Fast-slow Encoder based Transducer with Streaming Deliberation—0
Improving Frame-level Classifier for Word Timings with Non-peaky CTC in End-to-End Automatic Speech Recognition—0
Improving Generalization of Deep Neural Network Acoustic Models with Length Perturbation and N-best Based Label Smoothing—0
Improving Hybrid CTC/Attention End-to-end Speech Recognition with Pretrained Acoustic and Language Model—0
Improving Hypernasality Estimation with Automatic Speech Recognition in Cleft Palate Speech—0
Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap—0
An Investigation of Hybrid architectures for Low Resource Multilingual Speech Recognition system in Indian context—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