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 2701–2710 of 3012 papers

Title	Date	Tasks	Status
Simulating ASR errors for training SLU systems	May 1, 2018	Automatic Speech Recognition (ASR)Data Augmentation	—Unverified
Design and Development of Speech Corpora for Air Traffic Control Training	May 1, 2018	Automatic Speech Recognition (ASR)Speech Recognition	—Unverified
Data-Driven Pronunciation Modeling of Swiss German Dialectal Speech for Automatic Speech Recognition	May 1, 2018	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Creating Lithuanian and Latvian Speech Corpora from Inaccurately Annotated Web Data	May 1, 2018	Automatic Speech Recognition (ASR)Language Modeling	—Unverified
Speech Rate Calculations with Short Utterances: A Study from a Speech-to-Speech, Machine Translation Mediated Map Task	May 1, 2018	Automatic Speech Recognition (ASR)Machine Translation	—Unverified
Classification of Closely Related Sub-dialects of Arabic Using Support-Vector Machines	May 1, 2018	Automatic Speech Recognition (ASR)General Classification	—Unverified
Building Open Javanese and Sundanese Corpora for Multilingual Text-to-Speech	May 1, 2018	Automatic Speech Recognition (ASR)Speech Recognition	—Unverified
A Vietnamese Dialog Act Corpus Based on ISO 24617-2 standard	May 1, 2018	Automatic Speech Recognition (ASR)Emotion Recognition	—Unverified
The WAW Corpus: The First Corpus of Interpreted Speeches and their Translations for English and Arabic	May 1, 2018	Automatic Speech Recognition (ASR)Machine Translation	—Unverified
Towards an Automatic Assessment of Crowdsourced Data for NLU	May 1, 2018	Automatic Speech Recognition (ASR)Language Modeling	—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