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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 2301–2325 of 3012 papers

TitleStatusHype
Environment-aware Reconfigurable Noise Suppression—0
Deep Xi as a Front-End for Robust Automatic Speech Recognition—0
Joint Contextual Modeling for ASR Correction and Language Understanding—0
Submodular Rank Aggregation on Score-based Permutations for Distributed Automatic Speech RecognitionCode0
Lattice-based Improvements for Voice Triggering Using Graph Neural Networks—0
Low-rank Gradient Approximation For Memory-Efficient On-device Training of Deep Neural Network—0
Semi-supervised ASR by End-to-end Self-training—0
TLT-school: a Corpus of Non Native Children Speech—0
Sequence Labeling Approach to the Task of Sentence Boundary DetectionCode0
Transformer-based Online CTC/attention End-to-End Speech Recognition Architecture—0
Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses—0
Improving Dysarthric Speech Intelligibility Using Cycle-consistent Adversarial Training—0
Open Challenge for Correcting Errors of Speech Recognition Systems—0
Streaming automatic speech recognition with the transformer model—0
Audio-visual Recognition of Overlapped speech for the LRS2 dataset—0
Investigation and Analysis of Hyper and Hypo neuron pruning to selectively update neurons during Unsupervised Adaptation—0
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends—0
Speaker-aware speech-transformer—0
Attention based on-device streaming speech recognition with large speech corpus—0
EEG based Continuous Speech Recognition using Transformers—0
power-law nonlinearity with maximally uniform distribution criterion for improved neural network training in automatic speech recognition—0
Role of non-linear data processing on speech recognition task in the framework of reservoir computing—0
Statistical Testing on ASR Performance via Blockwise Bootstrap—0
Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems—0
A Cycle-GAN Approach to Model Natural Perturbations in Speech for ASR Applications—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