Topic
data-augmentation
Improving End-to-End Speech Recognition for Dysarthric Speech through In-Domain Data Augmentation
A study by Sapkota et al. explores data augmentation techniques for dysarthric automatic speech recognition (ASR) by fine-tuning the end-to-end Wav2Vec2 model. Four methods—Speaking-Rate Modification, Pitch Modification, Formant Modification, and vocal tract Length Perturbation—were tested across severity levels, achieving relative WER reductions of 30.02%, 16.64%, and 15.47% for low, medium, and high severity respectively.
ArtBoost: Synthetic Data Augmentation Boosts Acoustic-to-Articulatory Inversion with Limited Real Data
A new data augmentation strategy called ArtBoost leverages large-scale speech-mesh datasets from 3D facial animation to improve acoustic-to-articulatory inversion (AAI) models under limited EMA supervision. The method extracts pseudo articulatory trajectories from facial anchors and pre-trains models before fine-tuning on real data, yielding consistent gains in PCC and RMSE across architectures.
Data Augmentations Offer Path to Efficient Language Model Pretraining Under Data Constraints
As AI labs face a data ceiling where compute capacity outpaces new high-quality text, researchers propose data augmentations to enable productive multi-epoch training on fixed corpora. Three categories—token-level noise, sequence permutations, and target offset prediction—are shown to delay overfitting and lower validation loss compared to standard autoregressive pretraining. Random token replacement achieved the best minimum loss among individual methods, with combined augmentations further improving results.