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

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Showing 1401–1450 of 3012 papers

TitleStatusHype
Face-Dubbing++: Lip-Synchronous, Voice Preserving Translation of Videos—0
Extreme Encoder Output Frame Rate Reduction: Improving Computational Latencies of Large End-to-End Models—0
Bypass Temporal Classification: Weakly Supervised Automatic Speech Recognition with Imperfect Transcripts—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
Adversarial Joint Training with Self-Attention Mechanism for Robust End-to-End Speech Recognition—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
Extracting Domain Invariant Features by Unsupervised Learning for Robust Automatic Speech Recognition—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
Extracting Biomedical Entities from Noisy Audio Transcripts—0
Improving Accented Speech Recognition with Multi-Domain Training—0
BUT System for the MLC-SLM Challenge—0
Improving accuracy of rare words for RNN-Transducer through unigram shallow fusion—0
Extending Recurrent Neural Aligner for Streaming End-to-End Speech Recognition in Mandarin—0
BUT Opensat 2019 Speech Recognition System—0
A Probabilistic Framework for Representing Dialog Systems and Entropy-Based Dialog Management through Dynamic Stochastic State Evolution—0
Improving Black-box Speech Recognition using Semantic Parsing—0
Improving callsign recognition with air-surveillance data in air-traffic communication—0
Improving Character Error Rate Is Not Equal to Having Clean Speech: Speech Enhancement for ASR Systems with Black-box Acoustic Models—0
Improving Code-switched ASR with Linguistic Information—0
Improving Code-Switching and Named Entity Recognition in ASR with Speech Editing based Data Augmentation—0
Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR—0
Improving Confidence Estimation on Out-of-Domain Data for End-to-End Speech Recognition—0
Exponentially Decaying Bag-of-Words Input Features for Feed-Forward Neural Network in Statistical Machine Translation—0
Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation—0
Improving CTC-AED model with integrated-CTC and auxiliary loss regularization—0
Improving CTC-based ASR Models with Gated Interlayer Collaboration—0
Building state-of-the-art distant speech recognition using the CHiME-4 challenge with a setup of speech enhancement baseline—0
Improving Data Driven Inverse Text Normalization using Data Augmentation—0
Improving Distinction between ASR Errors and Speech Disfluencies with Feature Space Interpolation—0
Improving Dysarthric Speech Intelligibility Using Cycle-consistent Adversarial Training—0
Improving EEG based Continuous Speech Recognition—0
Improving End-to-End Bangla Speech Recognition with Semi-supervised Training—0
Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis—0
Improving End-to-end Speech Recognition with Pronunciation-assisted Sub-word Modeling—0
Exploring WavLM Back-ends for Speech Spoofing and Deepfake Detection—0
Improving Fast-slow Encoder based Transducer with Streaming Deliberation—0
Improving Frame-level Classifier for Word Timings with Non-peaky CTC in End-to-End Automatic Speech Recognition—0
Improving Generalization of Deep Neural Network Acoustic Models with Length Perturbation and N-best Based Label Smoothing—0
Improving Hybrid CTC/Attention End-to-end Speech Recognition with Pretrained Acoustic and Language Model—0
Improving Hypernasality Estimation with Automatic Speech Recognition in Cleft Palate Speech—0
Exploring Transfer Learning For End-to-End Spoken Language Understanding—0
Building Robust Spoken Language Understanding by Cross Attention between Phoneme Sequence and ASR Hypothesis—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