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 801–850 of 3012 papers

TitleStatusHype
Alignment Knowledge Distillation for Online Streaming Attention-based Speech Recognition—0
CTC-DRO: Robust Optimization for Reducing Language Disparities in Speech Recognition—0
A CTC Triggered Siamese Network with Spatial-Temporal Dropout for Speech Recognition—0
CTC Variations Through New WFST Topologies—0
Cumulative Adaptation for BLSTM Acoustic Models—0
CUNI Neural ASR with Phoneme-Level Intermediate Step for -Native at IWSLT 2020—0
Correlated Bigram LSA for Unsupervised Language Model Adaptation—0
Cycle-consistency training for end-to-end speech recognition—0
Cycle-Consistent GAN Front-End to Improve ASR Robustness to Perturbed Speech—0
Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Model—0
Correction Focused Language Model Training for Speech Recognition—0
Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models—0
Alignment-Free Training for Transducer-based Multi-Talker ASR—0
Corpus Phonetics Tutorial—0
Data Augmentation for End-to-end Code-switching Speech Recognition—0
Data Augmentation for End-to-End Speech Translation: FBK@IWSLT ‘19—0
Data Augmentation for Low-Resource Quechua ASR Improvement—0
Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors—0
Corpus Generation for Voice Command in Smart Home and the Effect of Speech Synthesis on End-to-End SLU—0
Attention-based ASR with Lightweight and Dynamic Convolutions—0
Data Augmentation with Locally-time Reversed Speech for Automatic Speech Recognition—0
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer—0
Corpora for Cross-Language Information Retrieval in Six Less-Resourced Languages—0
Data-Driven Pronunciation Modeling of Swiss German Dialectal Speech for Automatic Speech Recognition—0
CORILGA: a Galician Multilevel Annotated Speech Corpus for Linguistic Analysis—0
Alignment Entropy Regularization—0
Data-selective Transfer Learning for Multi-Domain Speech Recognition—0
DCF-DS: Deep Cascade Fusion of Diarization and Separation for Speech Recognition under Realistic Single-Channel Conditions—0
DCTX-Conformer: Dynamic context carry-over for low latency unified streaming and non-streaming Conformer ASR—0
DECCA Repurposed: Detecting transcription inconsistencies without an orthographic standard—0
A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition—0
A CLARIN Transcription Portal for Interview Data—0
Decoder-only Architecture for Streaming End-to-end Speech Recognition—0
Decoupled Federated Learning for ASR with Non-IID Data—0
3-D Feature and Acoustic Modeling for Far-Field Speech Recognition—0
Decoupling Pronunciation and Language for End-to-end Code-switching Automatic Speech Recognition—0
Decoupling recognition and transcription in Mandarin ASR—0
On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognition—0
Deep CLAS: Deep Contextual Listen, Attend and Spell—0
DeepCon: An End-to-End Multilingual Toolkit for Automatic Minuting of Multi-Party Dialogues—0
Deep context: end-to-end contextual speech recognition—0
Improving OOV Detection and Resolution with External Language Models in Acoustic-to-Word ASR—0
Listening while Speaking and Visualizing: Improving ASR through Multimodal Chain—0
Deep Learning Based Dereverberation of Temporal Envelopesfor Robust Speech Recognition—0
Deep Learning based Multi-Source Localization with Source Splitting and its Effectiveness in Multi-Talker Speech Recognition—0
Attacks as Defenses: Designing Robust Audio CAPTCHAs Using Attacks on Automatic Speech Recognition Systems—0
Deep Learning for Dialogue Systems—0
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments—0
Conv-Transformer Transducer: Low Latency, Low Frame Rate, Streamable End-to-End Speech Recognition—0
Convolutional Variational Autoencoders for Spectrogram Compression in Automatic Speech Recognition—0
Show:102550
← PrevPage 17 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