GLaS-JEPA: Gaussian-Regularized Speech SSL without Engineered Prediction Targets
Speech self-supervised learning aims to learn general-purpose representations for downstream speech tasks. However, current approaches rely on complex, carefully designed prediction targets. We challenge this necessity with GLaS-JEPA, a framework that directly predicts the current encoder's continuous representations at masked positions, without contrastive learning, discrete targets, or separate EMA target encoders. We prevent representation collapse using SIGReg representation-space regularization, eliminating the need for engineered target-generation mechanisms. Pretrained on 960 hours of LibriSpeech, our 57M-parameter model achieves a 6.89% WER on frozen-encoder SUPERB ASR and a 25.87...
| Authors | Gaspard Botté, Séverin Baroudi, Samir Sadok et al. |
|---|---|
| Primary category | eess.AS |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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