Self-supervised learning (SSL) for speech and audio has largely progressed along separate tracks: speech models emphasise time-domain prediction, whereas audio representation learning has focused on time-frequency patterns. This separation creates a compatibility gap, limiting cross-domain generalization. In this work,...
Geeth George, E. AmeenudeenP, Hrishikesh H. Pillai et al.· 0 citations
It is found that imagined-speech decoding benefits more from the language model than listened-speech decoding, and the best-performing balance between neural and language-model evidence shifts toward the language model, and the gain over neural-only decoding is larger.
Maryam Maghsoudi, Sai Samrat Kankanala, S. Shamma et al.· 0 citations
In these experiments, BiG-SURE improves average abstention AUROC over prior black-box uncertainty estimators, while remaining simple, unsupervised, and applicable to black-box model settings.