SOTAVerified

Accented Speech Recognition

Papers

Showing 11–20 of 20 papers

TitleStatusHype
Domain Adversarial Training for Accented Speech Recognition—0
GE2E-AC: Generalized End-to-End Loss Training for Accent Classification—0
Improving Accented Speech Recognition using Data Augmentation based on Unsupervised Text-to-Speech Synthesis—0
Improving Accented Speech Recognition with Multi-Domain Training—0
Improving Accent Identification and Accented Speech Recognition Under a Framework of Self-supervised Learning—0
Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis—0
Low-resource Accent Classification in Geographically-proximate Settings: A Forensic and Sociophonetics Perspective—0
Multi-pass Training and Cross-information Fusion for Low-resource End-to-end Accented Speech Recognition—0
Coupled Training of Sequence-to-Sequence Models for Accented Speech RecognitionCode0
Advancing African-Accented Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR ModelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error15.01—Unverified
2Deep Speech 2Percentage error7.55—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error28.46—Unverified
2Deep Speech 2Percentage error13.56—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error31.2—Unverified
2Deep Speech 2Percentage error17.55—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error45.35—Unverified
2Deep Speech 2Percentage error22.44—Unverified