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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 1401–1425 of 3012 papers

TitleStatusHype
Measuring the Impact of Individual Domain Factors in Self-Supervised Pre-Training—0
A Conformer Based Acoustic Model for Robust Automatic Speech Recognition—0
Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR—0
Integrating Text Inputs For Training and Adapting RNN Transducer ASR Models—0
A Survey of Multilingual Models for Automatic Speech Recognition—0
Language technology practitioners as language managers: arbitrating data bias and predictive bias in ASR—0
Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech RecognitionCode1
Towards Better Meta-Initialization with Task Augmentation for Kindergarten-aged Speech Recognition—0
Ask2Mask: Guided Data Selection for Masked Speech Modeling—0
Differentially Private Speaker Anonymization—0
Korean Tokenization for Beam Search Rescoring in Speech Recognition—0
Improving CTC-based speech recognition via knowledge transferring from pre-trained language modelsCode0
VADOI:Voice-Activity-Detection Overlapping Inference For End-to-end Long-form Speech Recognition—0
r-G2P: Evaluating and Enhancing Robustness of Grapheme to Phoneme Conversion by Controlled noise introducing and Contextual information incorporation—0
Speaker Adaptation Using Spectro-Temporal Deep Features for Dysarthric and Elderly Speech Recognition—0
SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their Descriptions—0
Domain Adaptation of low-resource Target-Domain models using well-trained ASR Conformer Models—0
'Beach' to 'Bitch': Inadvertent Unsafe Transcription of Kids' Content on YouTube—0
Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition—0
MLP-ASR: Sequence-length agnostic all-MLP architectures for speech recognition—0
AISHELL-NER: Named Entity Recognition from Chinese SpeechCode1
Conversational Speech Recognition By Learning Conversation-level Characteristics—0
Knowledge Transfer from Large-scale Pretrained Language Models to End-to-end Speech Recognizers—0
ADIMA: Abuse Detection In Multilingual AudioCode0
Multi-style Training for South African Call Centre Audio—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