Soft Clustering Anchors for Self-Supervised Speech Representation Learning in Joint Embedding Prediction Architectures
Abstract
GMM-Anchored JEPA fits soft clusters to acoustic features and uses them as auxiliary targets during self-supervised speech training. A changing supervision weight balances this grounding signal with latent prediction, avoiding iterative offline reclustering.
Type
Publication
arXiv preprint
Related work: S-JEPA is the later speech study with a continuous two-phase training procedure and online clustering targets.
Ravid Shwartz-Ziv
AI Researcher
AI researcher at Meta MSL with a background in information theory and computational neuroscience, working on world models, memory, and compression. Former Assistant Professor and Faculty Fellow at NYU, collaborating on research across academia and industry.