SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1851–1875 of 3012 papers

TitleStatusHype
Flexi-Transducer: Optimizing Latency, Accuracy and Compute forMulti-Domain On-Device Scenarios—0
Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding—0
End-to-End Speaker-Attributed ASR with Transformer—0
Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition—0
Speaker conditioned acoustic modeling for multi-speaker conversational ASR—0
Talk, Don't Write: A Study of Direct Speech-Based Image Retrieval—0
Towards Lifelong Learning of End-to-end ASR—0
Adversarial Joint Training with Self-Attention Mechanism for Robust End-to-End Speech Recognition—0
ExKaldi-RT: A Real-Time Automatic Speech Recognition Extension Toolkit of KaldiCode1
On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASRCode0
Context-sensitive evaluation of automatic speech recognition: considering user experience & language variation—0
Dialect Identification through Adversarial Learning and Knowledge Distillation on Romanian BERT—0
Tutorial Proposal: End-to-End Speech Translation—0
Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yol\'oxochitl Mixtec—0
Multilingual and code-switching ASR challenges for low resource Indian languagesCode1
Configurable Privacy-Preserving Automatic Speech Recognition—0
Multi-Encoder Learning and Stream Fusion for Transformer-Based End-to-End Automatic Speech Recognition—0
Large-Scale Pre-Training of End-to-End Multi-Talker ASR for Meeting Transcription with Single Distant Microphone—0
Integer-only Zero-shot Quantization for Efficient Speech RecognitionCode1
Adversarial Attacks and Defenses for Speech Recognition Systems—0
Multiple-hypothesis CTC-based semi-supervised adaptation of end-to-end speech recognition—0
Quantifying Bias in Automatic Speech RecognitionCode0
BART based semantic correction for Mandarin automatic speech recognition system—0
Construction of a Large-scale Japanese ASR Corpus on TV Recordings—0
Leveraging pre-trained representations to improve access to untranscribed speech from endangered languagesCode1
Show:102550
← PrevPage 75 of 121Next →
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