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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

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Showing 2501–2525 of 3012 papers

TitleStatusHype
Universal Adversarial Perturbations for Speech Recognition Systems—0
Analysis of Deep Clustering as Preprocessing for Automatic Speech Recognition of Sparsely Overlapping Speech—0
A Hardware-Oriented and Memory-Efficient Method for CTC Decoding—0
Deep Learning for Audio Signal ProcessingCode0
Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text—0
English Broadcast News Speech Recognition by Humans and Machines—0
Adversarial Speaker Adaptation—0
Attentive Adversarial Learning for Domain-Invariant Training—0
Multi-Geometry Spatial Acoustic Modeling for Distant Speech Recognition—0
Frequency Domain Multi-channel Acoustic Modeling for Distant Speech Recognition—0
Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors—0
Realizing Petabyte Scale Acoustic ModelingCode0
Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances—0
An Investigation of End-to-End Multichannel Speech Recognition for Reverberant and Mismatch Conditions—0
TTS Skins: Speaker Conversion via ASR—0
Hard Sample Mining for the Improved Retraining of Automatic Speech Recognition—0
Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation—0
A Multi-Task Learning Framework for Overcoming the Catastrophic Forgetting in Automatic Speech Recognition—0
STC Speaker Recognition Systems for the VOiCES From a Distance Challenge—0
Unsupervised Speech Domain Adaptation Based on Disentangled Representation Learning for Robust Speech RecognitionCode0
Distributed Deep Learning Strategies For Automatic Speech Recognition—0
Performance Monitoring for End-to-End Speech Recognition—0
Exploring Methods for the Automatic Detection of Errors in Manual Transcription—0
Constrained Output Embeddings for End-to-End Code-Switching Speech Recognition with Only Monolingual Data—0
Spoken Language Intent Detection using Confusion2VecCode0
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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