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 2011–2020 of 3012 papers

Title	Date	Tasks	Status
Listening Comprehension over Argumentative Content	Oct 1, 2018	Automatic Speech Recognition (ASR)Machine Reading Comprehension	—Unverified
Listening while Speaking: Speech Chain by Deep Learning	Jul 16, 2017	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Listen with Intent: Improving Speech Recognition with Audio-to-Intent Front-End	May 14, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
LiSTra, Automatic Speech Translation: English to Lingala case study	May 16, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
LiSTra Automatic Speech Translation: English to Lingala Case Study	Jun 1, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
LiteVSR: Efficient Visual Speech Recognition by Learning from Speech Representations of Unlabeled Data	Dec 15, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
LiveCC: Learning Video LLM with Streaming Speech Transcription at Scale	Jan 1, 2025	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
LLM-based phoneme-to-grapheme for phoneme-based speech recognition	Jun 5, 2025	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
LM-SPT: LM-Aligned Semantic Distillation for Speech Tokenization	Jun 20, 2025	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Local Feature or Mel Frequency Cepstral Coefficients - Which One is Better for MLN-Based Bangla Speech Recognition?	Oct 5, 2013	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—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