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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 2851–2875 of 3012 papers

TitleStatusHype
Bayesian Learning of LF-MMI Trained Time Delay Neural Networks for Speech Recognition—0
Bayes Risk Transducer: Transducer with Controllable Alignment Prediction—0
BayesSpeech: A Bayesian Transformer Network for Automatic Speech Recognition—0
BBS-KWS:The Mandarin Keyword Spotting System Won the Video Keyword Wakeup Challenge—0
BCN2BRNO: ASR System Fusion for Albayzin 2020 Speech to Text Challenge—0
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian—0
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian—0
'Beach' to 'Bitch': Inadvertent Unsafe Transcription of Kids' Content on YouTube—0
BECTRA: Transducer-based End-to-End ASR with BERT-Enhanced Encoder—0
Benchmarking Automatic Speech Recognition coupled LLM Modules for Medical Diagnostics—0
Benchmarking Evaluation Metrics for Code-Switching Automatic Speech Recognition—0
Benchmarking Foundation Speech and Language Models for Alzheimer's Disease and Related Dementia Detection from Spontaneous Speech—0
Benchmarking Japanese Speech Recognition on ASR-LLM Setups with Multi-Pass Augmented Generative Error Correction—0
Benchmarking LF-MMI, CTC and RNN-T Criteria for Streaming ASR—0
Benchmarking Rotary Position Embeddings for Automatic Speech Recognition—0
Bengali Common Voice Speech Dataset for Automatic Speech Recognition—0
Best of Both Worlds: Multi-task Audio-Visual Automatic Speech Recognition and Active Speaker Detection—0
Best of Both Worlds: Robust Accented Speech Recognition with Adversarial Transfer Learning—0
Better Pseudo-labeling with Multi-ASR Fusion and Error Correction by SpeechLLM—0
Better Transcription of UK Supreme Court Hearings—0
Beyond Binary: Multiclass Paraphasia Detection with Generative Pretrained Transformers and End-to-End Models—0
Beyond Manual Transcripts: The Potential of Automated Speech Recognition Errors in Improving Alzheimer's Disease Detection—0
Beyond Universal Transformer: block reusing with adaptor in Transformer for automatic speech recognition—0
Bi-APC: Bidirectional Autoregressive Predictive Coding for Unsupervised Pre-training and Its Application to Children's ASR—0
Biased Self-supervised learning for ASR—0
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All datasetsLRS2RealMANSagaleeHUI speech corpusLRS3-TEDM-AILabs speech datasetThe Spoken Wikipedia CorporaVoxforge GermanVoxPopuli

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TM-CTCTest WER10.1—Unverified
2TM-seq2seqTest WER9.7—Unverified
3CTC/attentionTest WER8.2—Unverified
4LF-MMI TDNNTest WER6.7—Unverified
5Whisper-LLaMATest WER6.6—Unverified
6End2end ConformerTest WER3.9—Unverified
7End2end ConformerTest WER3.7—Unverified
8MoCo + wav2vec (w/o extLM)Test WER2.7—Unverified
9CTC/AttentionTest WER1.5—Unverified
10WhisperTest WER1.3—Unverified
#ModelMetricClaimedVerifiedStatus
1SpatialNetCER14.5—Unverified
2CleanMel-L-maskCER14.4—Unverified
#ModelMetricClaimedVerifiedStatus
1ConformerTest WER15.32—Unverified
2Whisper-largev3-finetunedTest WER10.82—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)1.89—Unverified
#ModelMetricClaimedVerifiedStatus
1DistillAVWER1.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)4.28—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)8.04—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)3.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer Transducer (German)WER (%)8.98—Unverified