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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 1151–1200 of 3012 papers

TitleStatusHype
Improving Mandarin Speech Recogntion with Block-augmented TransformerCode1
ASR Error Detection via Audio-Transcript entailment—0
Transfer Learning of wav2vec 2.0 for Automatic Lyric TranscriptionCode1
When Is TTS Augmentation Through a Pivot Language Useful?Code0
Improving Data Driven Inverse Text Normalization using Data Augmentation—0
ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding—0
ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale—0
End-to-End Spoken Language Understanding: Performance analyses of a voice command task in a low resource setting—0
Reducing Geographic Disparities in Automatic Speech Recognition via Elastic Weight Consolidation—0
Direction-Aware Joint Adaptation of Neural Speech Enhancement and Recognition in Real Multiparty Conversational Environments—0
Deep versus Wide: An Analysis of Student Architectures for Task-Agnostic Knowledge Distillation of Self-Supervised Speech Models—0
Data Augmentation for Low-Resource Quechua ASR Improvement—0
MM-ALT: A Multimodal Automatic Lyric Transcription SystemCode1
Huqariq: A Multilingual Speech Corpus of Native Languages of Peru for Speech Recognition—0
End-to-end speech recognition modeling from de-identified data—0
Online Continual Learning of End-to-End Speech Recognition Models—0
Speaker Anonymization with Phonetic Intermediate Representations—0
pMCT: Patched Multi-Condition Training for Robust Speech Recognition—0
Investigating the Impact of Cross-lingual Acoustic-Phonetic Similarities on Multilingual Speech Recognition—0
End-to-end Speech-to-Punctuated-Text Recognition—0
Improving Streaming End-to-End ASR on Transformer-based Causal Models with Encoder States Revision Strategies—0
Compute Cost Amortized Transformer for Streaming ASR—0
Vietnamese Capitalization and Punctuation Recovery ModelsCode0
Leveraging Acoustic Contextual Representation by Audio-textual Cross-modal Learning for Conversational ASR—0
Improving Transformer-based Conversational ASR by Inter-Sentential Attention Mechanism—0
Tree-constrained Pointer Generator with Graph Neural Network Encodings for Contextual Speech Recognition—0
Exploring the Effect of Dialect Mismatched Language Models in Telugu Automatic Speech Recognition—0
Non-Autoregressive Chinese ASR Error Correction with Phonological Training—0
BehancePR: A Punctuation Restoration Dataset for Livestreaming Video TranscriptCode0
Activity focused Speech Recognition of Preschool Children in Early Childhood Classrooms—0
Improving Low-Resource Speech Recognition with Pretrained Speech Models: Continued Pretraining vs. Semi-Supervised Training—0
Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End ASR Models—0
FeaRLESS: Feature Refinement Loss for Ensembling Self-Supervised Learning Features in Robust End-to-end Speech Recognition—0
STOP: A dataset for Spoken Task Oriented Semantic Parsing—0
The THUEE System Description for the IARPA OpenASR21 Challenge—0
Space-Efficient Representation of Entity-centric Query Language Models—0
Bengali Common Voice Speech Dataset for Automatic Speech Recognition—0
Challenges and Opportunities in Multi-device Speech Processing—0
TALCS: An Open-Source Mandarin-English Code-Switching Corpus and a Speech Recognition Baseline—0
Improving the Training Recipe for a Robust Conformer-based Hybrid Model—0
Meta Auxiliary Learning for Low-resource Spoken Language Understanding—0
Annotated Speech Corpus for Low Resource Indian Languages: Awadhi, Bhojpuri, Braj and Magahi—0
On Comparison of Encoders for Attention based End to End Speech Recognition in Standalone and Rescoring Mode—0
Distilling a Pretrained Language Model to a Multilingual ASR ModelCode1
TEVR: Improving Speech Recognition by Token Entropy Variance ReductionCode2
Confidence Score Based Conformer Speaker Adaptation for Speech Recognition—0
Two-pass Decoding and Cross-adaptation Based System Combination of End-to-end Conformer and Hybrid TDNN ASR Systems—0
A Simple Baseline for Domain Adaptation in End to End ASR Systems Using Synthetic Data—0
A Systematic Comparison of Phonetic Aware Techniques for Speech EnhancementCode1
Supervision-Guided Codebooks for Masked Prediction in Speech Pre-training—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