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 1401–1410 of 3012 papers

Title	Date	Tasks	Status
Adaptive Activation Network For Low Resource Multilingual Speech Recognition	May 28, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Acoustic-to-articulatory Speech Inversion with Multi-task Learning	May 27, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Punctuation Restoration in Spanish Customer Support Transcripts using Transfer Learning	May 27, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Joint Training of Speech Enhancement and Self-supervised Model for Noise-robust ASR	May 26, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Contextual Adapters for Personalized Speech Recognition in Neural Transducers	May 26, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Clinical Dialogue Transcription Error Correction using Seq2Seq Models	May 26, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Improving CTC-based ASR Models with Gated Interlayer Collaboration	May 25, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
On Building Spoken Language Understanding Systems for Low Resourced Languages	May 25, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Heterogeneous Reservoir Computing Models for Persian Speech Recognition	May 25, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech	May 25, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available

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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