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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 1126–1150 of 3012 papers

TitleStatusHype
Attention Enhanced Citrinet for Speech Recognition—0
A Language Agnostic Multilingual Streaming On-Device ASR System—0
Investigating data partitioning strategies for crosslinguistic low-resource ASR evaluation—0
IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languagesCode1
DualVoice: Speech Interaction that Discriminates between Normal and Whispered Voice Input—0
Are disentangled representations all you need to build speaker anonymization systems?—0
Analyzing Robustness of End-to-End Neural Models for Automatic Speech RecognitionCode0
Building a Public Domain Voice Database for Odia—0
Improving Hypernasality Estimation with Automatic Speech Recognition in Cleft Palate Speech—0
Comparison and Analysis of New Curriculum Criteria for End-to-End ASRCode0
ASR Error Correction with Constrained Decoding on Operation PredictionCode1
Thai Wav2Vec2.0 with CommonVoice V8Code0
Large vocabulary speech recognition for languages of Africa: multilingual modeling and self-supervised learning—0
Automatic Speech Recognition in German: A Detailed Error Analysis—0
Adversarial Attacks on ASR Systems: An Overview—0
Global Performance Disparities Between English-Language Accents in Automatic Speech Recognition—0
DENT-DDSP: Data-efficient noisy speech generator using differentiable digital signal processors for explicit distortion modelling and noise-robust speech recognitionCode1
Domain Specific Wav2vec 2.0 Fine-tuning For The SE&R 2022 ChallengeCode0
Multiple-hypothesis RNN-T Loss for Unsupervised Fine-tuning and Self-training of Neural Transducer—0
Pronunciation-aware unique character encoding for RNN Transducer-based Mandarin speech recognition—0
Thutmose Tagger: Single-pass neural model for Inverse Text Normalization—0
Subword Dictionary Learning and Segmentation Techniques for Automatic Speech Recognition in Tamil and Kannada—0
Knowledge-driven Subword Grammar Modeling for Automatic Speech Recognition in Tamil and Kannada—0
Unsupervised data selection for Speech Recognition with contrastive loss ratios—0
Learning a Dual-Mode Speech Recognition Model via Self-Pruning—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