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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 2276–2300 of 3012 papers

TitleStatusHype
ArzEn: A Speech Corpus for Code-switched Egyptian Arabic-English—0
On Construction of the ASR-oriented Indian English Pronunciation Dictionary—0
A CLARIN Transcription Portal for Interview Data—0
Evaluating and Improving Child-Directed Automatic Speech Recognition—0
Parallel Corpus for Japanese Spoken-to-Written Style Conversion—0
RSC: A Romanian Read Speech Corpus for Automatic Speech Recognition—0
Multiresolution and Multimodal Speech Recognition with Transformers—0
Beyond Instructional Videos: Probing for More Diverse Visual-Textual Grounding on YouTubeCode0
Jointly Trained Transformers models for Spoken Language Translation—0
End-to-end speech-to-dialog-act recognition—0
A Study of Non-autoregressive Model for Sequence Generation—0
ESPnet-ST: All-in-One Speech Translation Toolkit—0
Speaker Diarization with Lexical Information—0
Punctuation Prediction in Spontaneous Conversations: Can We Mitigate ASR Errors with Retrofitted Word Embeddings?—0
Improving Readability for Automatic Speech Recognition Transcription—0
Semi-supervised acoustic modelling for five-lingual code-switched ASR using automatically-segmented soap opera speech—0
Homophone-based Label Smoothing in End-to-End Automatic Speech Recognition—0
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement—0
Low Latency ASR for Simultaneous Speech Translation—0
Training for Speech Recognition on Coprocessors—0
High-Accuracy and Low-Latency Speech Recognition with Two-Head Contextual Layer Trajectory LSTM Model—0
Deliberation Model Based Two-Pass End-to-End Speech Recognition—0
ASR Error Correction and Domain Adaptation Using Machine Translation—0
Hybrid Autoregressive Transducer (hat)—0
Improving noise robust automatic speech recognition with single-channel time-domain enhancement network—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