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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 301–325 of 3012 papers

TitleStatusHype
An investigation of modularity for noise robustness in conformer-based ASR—0
Retrieval Augmented Correction of Named Entity Speech Recognition Errors—0
Findings of the 2024 Mandarin Stuttering Event Detection and Automatic Speech Recognition Challenge—0
Evaluation of real-time transcriptions using end-to-end ASR models—0
Exploring WavLM Back-ends for Speech Spoofing and Deepfake Detection—0
Probing self-attention in self-supervised speech models for cross-linguistic differences—0
Quantification of stylistic differences in human- and ASR-produced transcripts of African American English—0
What is lost in Normalization? Exploring Pitfalls in Multilingual ASR Model Evaluations—0
Reassessing Noise Augmentation Methods in the Context of Adversarial Speech—0
Temporal Order Preserved Optimal Transport-based Cross-modal Knowledge Transfer Learning for ASR—0
VoxHakka: A Dialectally Diverse Multi-speaker Text-to-Speech System for Taiwanese Hakka—0
Resource-Efficient Adaptation of Speech Foundation Models for Multi-Speaker ASR—0
Comparing Discrete and Continuous Space LLMs for Speech Recognition—0
Serialized Speech Information Guidance with Overlapped Encoding Separation for Multi-Speaker Automatic Speech Recognition—0
Speaker Tagging Correction With Non-Autoregressive Language Models—0
Advancing Multi-talker ASR Performance with Large Language Models—0
Measuring the Accuracy of Automatic Speech Recognition SolutionsCode0
Benchmarking Japanese Speech Recognition on ASR-LLM Setups with Multi-Pass Augmented Generative Error Correction—0
Beyond Levenshtein: Leveraging Multiple Algorithms for Robust Word Error Rate Computations And Granular Error ClassificationsCode0
Automatic recognition and detection of aphasic natural speech—0
Self-supervised Speech Representations Still Struggle with African American Vernacular EnglishCode0
MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues—0
Focused Discriminative Training For Streaming CTC-Trained Automatic Speech Recognition Models—0
The State of Commercial Automatic French Legal Speech Recognition Systems and their Impact on Court Reporters et al—0
Parameter-Efficient Transfer Learning under Federated Learning for Automatic 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