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 1401–1425 of 3012 papers

TitleStatusHype
Advanced Long-context End-to-end Speech Recognition Using Context-expanded Transformers—0
Self-Supervised Learning for Multi-Channel Neural Transducer—0
Improved Conformer-based End-to-End Speech Recognition Using Neural Architecture Search—0
Improved Consistency Training for Semi-Supervised Sequence-to-Sequence ASR via Speech Chain Reconstruction and Self-Transcribing—0
Improved Contextual Recognition In Automatic Speech Recognition Systems By Semantic Lattice Rescoring—0
Improved far-field speech recognition using Joint Variational Autoencoder—0
Improved Language Identification Through Cross-Lingual Self-Supervised Learning—0
Improved low-resource Somali speech recognition by semi-supervised acoustic and language model training—0
Improved Mask-CTC for Non-Autoregressive End-to-End ASR—0
Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis—0
Improved Robustness to Disfluencies in RNN-Transducer Based Speech Recognition—0
Improved Self-Supervised Multilingual Speech Representation Learning Combined with Auxiliary Language Information—0
Improved Speech Pre-Training with Supervision-Enhanced Acoustic Unit—0
Improved Training Techniques for Online Neural Machine Translation—0
Improved Transcription and Indexing of Oral History Interviews for Digital Humanities Research—0
Improve Sinhala Speech Recognition Through e2e LF-MMI Model—0
Enhancing Lyrics Transcription on Music Mixtures with Consistency Loss—0
Improving Accented Speech Recognition with Multi-Domain Training—0
Improving Accent Identification and Accented Speech Recognition Under a Framework of Self-supervised Learning—0
Improving accuracy of rare words for RNN-Transducer through unigram shallow fusion—0
Improving ASR Contextual Biasing with Guided Attention—0
Confusion2Vec: Towards Enriching Vector Space Word Representations with Representational Ambiguities—0
Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap—0
Improving Black-box Speech Recognition using Semantic Parsing—0
An Investigation of Hybrid architectures for Low Resource Multilingual Speech Recognition system in Indian context—0
Show:102550
← PrevPage 57 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