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

Showing 226250 of 3012 papers

TitleStatusHype
A Toolbox for Construction and Analysis of Speech DatasetsCode1
NoRefER: a Referenceless Quality Metric for Automatic Speech Recognition via Semi-Supervised Language Model Fine-Tuning with Contrastive LearningCode1
Audio-Visual Representation Learning via Knowledge Distillation from Speech Foundation ModelsCode1
Audio-Visual Efficient Conformer for Robust Speech RecognitionCode1
Open Source Automatic Speech Recognition for GermanCode1
Attention-based Contextual Language Model Adaptation for Speech RecognitionCode1
Attention-based Audio-Visual Fusion for Robust Automatic Speech RecognitionCode1
PriMock57: A Dataset Of Primary Care Mock ConsultationsCode1
Punctuation Restoration using Transformer Models for High-and Low-Resource LanguagesCode1
Attentive Sequence-to-Sequence Learning for Diacritic Restoration of Yorùbá Language TextCode1
Automatic Disfluency Detection from Untranscribed SpeechCode1
REBORN: Reinforcement-Learned Boundary Segmentation with Iterative Training for Unsupervised ASRCode1
AVLnet: Learning Audio-Visual Language Representations from Instructional VideosCode1
Robust Data2vec: Noise-robust Speech Representation Learning for ASR by Combining Regression and Improved Contrastive LearningCode1
A Survey on Non-Autoregressive Generation for Neural Machine Translation and BeyondCode1
A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and EnglishCode1
A Systematic Comparison of Phonetic Aware Techniques for Speech EnhancementCode1
An Investigation of End-to-End Models for Robust Speech RecognitionCode1
SGEM: Test-Time Adaptation for Automatic Speech Recognition via Sequential-Level Generalized Entropy MinimizationCode1
Single-Channel Multi-Speaker Separation using Deep ClusteringCode1
Skit-S2I: An Indian Accented Speech to Intent datasetCode1
ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversionCode1
SoftCTC -- Semi-Supervised Learning for Text Recognition using Soft Pseudo-LabelsCode1
Speaker Recognition in the WildCode1
A Sidecar Separator Can Convert a Single-Talker Speech Recognition System to a Multi-Talker OneCode1
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Benchmark Results

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