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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 776–800 of 3012 papers

TitleStatusHype
Correlated Bigram LSA for Unsupervised Language Model Adaptation—0
Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Model—0
Correction Focused Language Model Training for Speech Recognition—0
Learning Speech Representation From Contrastive Token-Acoustic Pretraining—0
CPT-Boosted Wav2vec2.0: Towards Noise Robust Speech Recognition for Classroom Environments—0
CR-CTC: Consistency regularization on CTC for improved speech recognition—0
Creating Lithuanian and Latvian Speech Corpora from Inaccurately Annotated Web Data—0
Creating Spoken Dialog Systems in Ultra-Low Resourced Settings—0
Critical Appraisal of Artificial Intelligence-Mediated Communication—0
Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models—0
Cross-attention conformer for context modeling in speech enhancement for ASR—0
Cross-domain Single-channel Speech Enhancement Model with Bi-projection Fusion Module for Noise-robust ASR—0
Alignment-Free Training for Transducer-based Multi-Talker ASR—0
Corpus Phonetics Tutorial—0
Cross-lingual Embedding Clustering for Hierarchical Softmax in Low-Resource Multilingual Speech Recognition—0
Corpus Generation for Voice Command in Smart Home and the Effect of Speech Synthesis on End-to-End SLU—0
Cross-lingual studies of ASR errors: paradigms for perceptual evaluations—0
Cross-modal Alignment with Optimal Transport for CTC-based ASR—0
Cross-Modal ASR Post-Processing System for Error Correction and Utterance Rejection—0
Attention-based ASR with Lightweight and Dynamic Convolutions—0
Cross-Modal Transformer-Based Neural Correction Models for Automatic Speech Recognition—0
Cross-utterance Reranking Models with BERT and Graph Convolutional Networks for Conversational Speech Recognition—0
Cross-Utterance Language Models with Acoustic Error Sampling—0
Corpora for Cross-Language Information Retrieval in Six Less-Resourced Languages—0
CORILGA: a Galician Multilevel Annotated Speech Corpus for Linguistic Analysis—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