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 1051–1100 of 3012 papers

TitleStatusHype
UFO2: A unified pre-training framework for online and offline speech recognition—0
Monotonic segmental attention for automatic speech recognition—0
Linguistic-Enhanced Transformer with CTC Embedding for Speech Recognition—0
Time-Domain Speech Enhancement for Robust Automatic Speech Recognition—0
Does Joint Training Really Help Cascaded Speech Translation?Code0
Brouhaha: multi-task training for voice activity detection, speech-to-noise ratio, and C50 room acoustics estimationCode1
ESB: A Benchmark For Multi-Domain End-to-End Speech RecognitionCode1
Investigating self-supervised, weakly supervised and fully supervised training approaches for multi-domain automatic speech recognition: a study on Bangladeshi Bangla—0
Guided contrastive self-supervised pre-training for automatic speech recognition—0
Can Visual Context Improve Automatic Speech Recognition for an Embodied Agent?—0
Optimizing Bilingual Neural Transducer with Synthetic Code-switching Text Generation—0
Improving Semi-supervised End-to-end Automatic Speech Recognition using CycleGAN and Inter-domain Losses—0
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR—0
Discrete Cross-Modal Alignment Enables Zero-Shot Speech TranslationCode0
Language-agnostic Code-Switching in Sequence-To-Sequence Speech Recognition—0
Towards Relation Extraction From SpeechCode1
Sub-8-bit quantization for on-device speech recognition: a regularization-free approach—0
Continuous Pseudo-Labeling from the Start—0
Bringing NURC/SP to Digital Life: the Role of Open-source Automatic Speech Recognition ModelsCode0
LeVoice ASR Systems for the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge—0
Learning to Jointly Transcribe and Subtitle for End-to-End Spontaneous Speech Recognition—0
Experiments on Turkish ASR with Self-Supervised Speech Representation Learning—0
Can we use Common Voice to train a Multi-Speaker TTS system?Code1
Summary on the ISCSLP 2022 Chinese-English Code-Switching ASR Challenge—0
A context-aware knowledge transferring strategy for CTC-based ASRCode1
Automatic Speech Recognition of Low-Resource Languages Based on Chukchi—0
Scaling Up Deliberation for Multilingual ASR—0
Comparison of Soft and Hard Target RNN-T Distillation for Large-scale ASR—0
Streaming Punctuation for Long-form Dictation with Transformers—0
CTC Alignments Improve Autoregressive Translation—0
An Experimental Study on Private Aggregation of Teacher Ensemble Learning for End-to-End Speech Recognition—0
SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-trainingCode0
Cloud-based Automatic Speech Recognition Systems for Southeast Asian Languages—0
Pronunciation Modeling of Foreign Words for Mandarin ASR by Considering the Effect of Language Transfer—0
Damage Control During Domain Adaptation for Transducer Based Automatic Speech Recognition—0
JoeyS2T: Minimalistic Speech-to-Text Modeling with JoeyNMTCode1
CCC-wav2vec 2.0: Clustering aided Cross Contrastive Self-supervised learning of speech representationsCode1
Efficient acoustic feature transformation in mismatched environments using a Guided-GAN—0
Investigating the Impact of ASR Errors on Spoken Implicit Discourse Relation Recognition—0
Language-specific Effects on Automatic Speech Recognition Errors for World Englishes—0
Improving Code-switched ASR with Linguistic Information—0
Can We Train a Language Model Inside an End-to-End ASR Model? - Investigating Effective Implicit Language Modeling—0
Keyphrase Prediction from Video Transcripts: New Dataset and Directions—0
Zero-shot Disfluency Detection for Indian Languages—0
Multi-stage Progressive Compression of Conformer Transducer for On-device Speech Recognition—0
E-Branchformer: Branchformer with Enhanced merging for speech recognition—0
Blind Signal Dereverberation for Machine Speech Recognition—0
Adaptive Sparse and Monotonic Attention for Transformer-based Automatic Speech Recognition—0
TVLT: Textless Vision-Language TransformerCode1
An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts—0
Show:102550
← PrevPage 22 of 61Next →
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