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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 551–575 of 3012 papers

TitleStatusHype
DiarizationLM: Speaker Diarization Post-Processing with Large Language ModelsCode3
MLCA-AVSR: Multi-Layer Cross Attention Fusion based Audio-Visual Speech Recognition—0
ICMC-ASR: The ICASSP 2024 In-Car Multi-Channel Automatic Speech Recognition Challenge—0
TeLeS: Temporal Lexeme Similarity Score to Estimate Confidence in End-to-End ASRCode0
Task Oriented Dialogue as a Catalyst for Self-Supervised Automatic Speech RecognitionCode0
Hallucinations in Neural Automatic Speech Recognition: Identifying Errors and Hallucinatory Models—0
Towards Probing Contact Center Large Language Models—0
Exploring data augmentation in bias mitigation against non-native-accented speech—0
Multimodal Attention Merging for Improved Speech Recognition and Audio Event Classification—0
BLSTM-Based Confidence Estimation for End-to-End Speech Recognition—0
Stable Distillation: Regularizing Continued Pre-training for Low-Resource Automatic Speech RecognitionCode0
Lattice Rescoring Based on Large Ensemble of Complementary Neural Language Models—0
SpokesBiz -- an Open Corpus of Conversational Polish—0
OAVA: the open audio-visual archives aggregator—0
Seq2seq for Automatic Paraphasia Detection in Aphasic SpeechCode0
Conformer-Based Speech Recognition On Extreme Edge-Computing Devices—0
LiteVSR: Efficient Visual Speech Recognition by Learning from Speech Representations of Unlabeled Data—0
FastInject: Injecting Unpaired Text Data into CTC-based ASR training—0
USM-Lite: Quantization and Sparsity Aware Fine-tuning for Speech Recognition with Universal Speech Models—0
Extending Whisper with prompt tuning to target-speaker ASRCode1
Creating Spoken Dialog Systems in Ultra-Low Resourced Settings—0
ROSE: A Recognition-Oriented Speech Enhancement Framework in Air Traffic Control Using Multi-Objective LearningCode0
Integrating Pre-Trained Speech and Language Models for End-to-End Speech Recognition—0
Bigger is not Always Better: The Effect of Context Size on Speech Pre-TrainingCode0
End-to-End Speech-to-Text Translation: A Survey—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