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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 951–975 of 3012 papers

TitleStatusHype
Learning When to Trust Which Teacher for Weakly Supervised ASR—0
Mixture Encoder for Joint Speech Separation and Recognition—0
Exploring the Role of Audio in Video Captioning—0
Federated Self-Learning with Weak Supervision for Speech Recognition—0
Rehearsal-Free Online Continual Learning for Automatic Speech RecognitionCode0
MobileASR: A resource-aware on-device learning framework for user voice personalization applications on mobile phones—0
Lexical Speaker Error Correction: Leveraging Language Models for Speaker Diarization Error Correction—0
Unified model for code-switching speech recognition and language identification based on a concatenated tokenizer—0
Improving Code-Switching and Named Entity Recognition in ASR with Speech Editing based Data Augmentation—0
DCTX-Conformer: Dynamic context carry-over for low latency unified streaming and non-streaming Conformer ASR—0
Statistical Beamformer Exploiting Non-stationarity and Sparsity with Spatially Constrained ICA for Robust Speech Recognition—0
Multimodal Audio-textual Architecture for Robust Spoken Language Understanding—0
On the N-gram Approximation of Pre-trained Language Models—0
Impact of Experiencing Misrecognition by Teachable Agents on Learning and Rapport—0
Adversarial Training For Low-Resource Disfluency CorrectionCode0
Improving Frame-level Classifier for Word Timings with Non-peaky CTC in End-to-End Automatic Speech Recognition—0
A Theory of Unsupervised Speech RecognitionCode0
An ASR-Based Tutor for Learning to Read: How to Optimize Feedback to First Graders—0
A study on the impact of Self-Supervised Learning on automatic dysarthric speech assessment—0
Arabic Dysarthric Speech Recognition Using Adversarial and Signal-Based AugmentationCode0
Automatic Assessment of Oral Reading Accuracy for Reading Diagnostics—0
Improving Fairness and Robustness in End-to-End Speech Recognition through unsupervised clustering—0
Alzheimer Disease Classification through ASR-based Transcriptions: Exploring the Impact of Punctuation and Pauses—0
OTF: Optimal Transport based Fusion of Supervised and Self-Supervised Learning Models for Automatic Speech Recognition—0
Incorporating L2 Phonemes Using Articulatory Features for Robust Speech Recognition—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