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 1101–1110 of 3012 papers

Title	Date	Tasks	Status
A Hardware-Oriented and Memory-Efficient Method for CTC Decoding	May 8, 2019	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Comparison of Soft and Hard Target RNN-T Distillation for Large-scale ASR	Oct 11, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Comparison of Self-Supervised Speech Pre-Training Methods on Flemish Dutch	Sep 29, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems	May 16, 2025	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Comparison of Lattice-Free and Lattice-Based Sequence Discriminative Training Criteria for LVCSR	Jul 1, 2019	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary	Dec 1, 2016	Active LearningAutomatic Speech Recognition	—Unverified
ASR error management for improving spoken language understanding	May 26, 2017	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning	Sep 1, 2017	Automatic Speech Recognition (ASR)Dialogue Management	—Unverified
Acoustic Model Fusion for End-to-end Speech Recognition	Oct 10, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Accent Recognition with Hybrid Phonetic Features	May 5, 2021	Audio ClassificationAutomatic Speech Recognition	—Unverified

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All datasets LRS2 RealMAN Sagalee HUI speech corpus LRS3-TED M-AILabs speech dataset The Spoken Wikipedia Corpora Voxforge German VoxPopuli

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	TM-CTC	Test WER	10.1	—	Unverified
2	TM-seq2seq	Test WER	9.7	—	Unverified
3	CTC/attention	Test WER	8.2	—	Unverified
4	LF-MMI TDNN	Test WER	6.7	—	Unverified
5	Whisper-LLaMA	Test WER	6.6	—	Unverified
6	End2end Conformer	Test WER	3.9	—	Unverified
7	End2end Conformer	Test WER	3.7	—	Unverified
8	MoCo + wav2vec (w/o extLM)	Test WER	2.7	—	Unverified
9	CTC/Attention	Test WER	1.5	—	Unverified
10	Whisper	Test WER	1.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SpatialNet	CER	14.5	—	Unverified
2	CleanMel-L-mask	CER	14.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Conformer	Test WER	15.32	—	Unverified
2	Whisper-largev3-finetuned	Test WER	10.82	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Conformer Transducer	WER (%)	1.89	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DistillAV	WER	1.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Conformer Transducer	WER (%)	4.28	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Conformer Transducer	WER (%)	8.04	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Conformer Transducer	WER (%)	3.36	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Conformer Transducer (German)	WER (%)	8.98	—	Unverified