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 2841–2850 of 3012 papers

Title	Date	Tasks	Status
A Vietnamese Dialog Act Corpus Based on ISO 24617-2 standard	May 1, 2018	Automatic Speech Recognition (ASR)Emotion Recognition	—Unverified
A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers	Apr 16, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Babler - Data Collection from the Web to Support Speech Recognition and Keyword Search	Aug 1, 2016	Automatic Speech Recognition (ASR)Language Identification	—Unverified
Back-Translation-Style Data Augmentation for End-to-End ASR	Jul 28, 2018	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
BanglaNum -- A Public Dataset for Bengali Digit Recognition from Speech	Mar 20, 2024	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing N-gram Language Models	Sep 13, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
BART based semantic correction for Mandarin automatic speech recognition system	Mar 26, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
BA-SOT: Boundary-Aware Serialized Output Training for Multi-Talker ASR	May 23, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
BAT: Boundary aware transducer for memory-efficient and low-latency ASR	May 19, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Bayesian Language Model based on Mixture of Segmental Contexts for Spontaneous Utterances with Unexpected Words	Dec 1, 2016	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