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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 1851–1875 of 3012 papers

TitleStatusHype
Residual Energy-Based Models for End-to-End Speech Recognition—0
Residual Language Model for End-to-end Speech Recognition—0
Resolving Transcription Ambiguity in Spanish: A Hybrid Acoustic-Lexical System for Punctuation Restoration—0
Resource aware design of a deep convolutional-recurrent neural network for speech recognition through audio-visual sensor fusion—0
Resource-Efficient Adaptation of Speech Foundation Models for Multi-Speaker ASR—0
Resource-Efficient Transfer Learning From Speech Foundation Model Using Hierarchical Feature Fusion—0
Rethinking End-to-End Evaluation of Decomposable Tasks: A Case Study on Spoken Language Understanding—0
Rethinking Processing Distortions: Disentangling the Impact of Speech Enhancement Errors on Speech Recognition Performance—0
Retrieval Augmented Correction of Named Entity Speech Recognition Errors—0
Retrieval-Enhanced Few-Shot Prompting for Speech Event Extraction—0
Retrieve and Copy: Scaling ASR Personalization to Large Catalogs—0
Revisiting Acoustic Features for Robust ASR—0
Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systems—0
r-G2P: Evaluating and Enhancing Robustness of Grapheme to Phoneme Conversion by Controlled noise introducing and Contextual information incorporation—0
Richer Interpolative Smoothing Based on Modified Kneser-Ney Language Modeling—0
RIR-SF: Room Impulse Response Based Spatial Feature for Target Speech Recognition in Multi-Channel Multi-Speaker Scenarios—0
RNN-T For Latency Controlled ASR With Improved Beam Search—0
RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions—0
Roadmap towards Superhuman Speech Understanding using Large Language Models—0
ROAR: Reinforcing Original to Augmented Data Ratio Dynamics for Wav2Vec2.0 Based ASR—0
Robust ASR Error Correction with Conservative Data Filtering—0
Robust Automatic Speech Recognition via WavAugment Guided Phoneme Adversarial Training—0
Robust fine-tuning of speech recognition models via model merging: application to disordered speech—0
Robustifying automatic speech recognition by extracting slowly varying features—0
Robust Multi-channel Speech Recognition using Frequency Aligned 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