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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 1601–1625 of 3012 papers

TitleStatusHype
Improving Speech Recognition on Noisy Speech via Speech Enhancement with Multi-Discriminators CycleGAN—0
Improving Code-switching Language Modeling with Artificially Generated Texts using Cycle-consistent Adversarial Networks—0
Sequence-level self-learning with multiple hypotheses—0
Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition—0
Directed Speech Separation for Automatic Speech Recognition of Long Form Conversational Speech—0
Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systems—0
A study on native American English speech recognition by Indian listeners with varying word familiarity level—0
Catch Me If You Can: Blackbox Adversarial Attacks on Automatic Speech Recognition using Frequency Masking—0
BBS-KWS:The Mandarin Keyword Spotting System Won the Video Keyword Wakeup Challenge—0
A higher order Minkowski loss for improved prediction ability of acoustic model in ASR—0
A Mixture of Expert Based Deep Neural Network for Improved ASR—0
Loss Landscape Dependent Self-Adjusting Learning Rates in Decentralized Stochastic Gradient Descent—0
IE-CPS Lexicon: An Automatic Speech Recognition Oriented Indian-English Pronunciation Dictionary—0
An Investigation of Hybrid architectures for Low Resource Multilingual Speech Recognition system in Indian context—0
Analysis of Manipuri Tones in ManiTo: A Tonal Contrast Database—0
An Experiment on Speech-to-Text Translation Systems for Manipuri to English on Low Resource Setting—0
Speech-T: Transducer for Text to Speech and Beyond—0
Predicting lexical skills from oral reading with acoustic measures—0
Improve Sinhala Speech Recognition Through e2e LF-MMI Model—0
Do We Still Need Automatic Speech Recognition for Spoken Language Understanding?—0
Effect of noise suppression losses on speech distortion and ASR performance—0
Multi-Channel Multi-Speaker ASR Using 3D Spatial Feature—0
Capitalization and Punctuation Restoration: a Survey—0
Deep Spoken Keyword Spotting: An Overview—0
Switching Independent Vector Analysis and Its Extension to Blind and Spatially Guided Convolutional Beamforming Algorithms—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