S-JEPA uses soft GMM posteriors in a JEPA framework for self-supervised speech learning, achieving lowest WER below 90M parameters without offline re-clustering.
DeCoAR 2.0: Deep Contextualized Acoustic Repre- sentations with Vector Quantization
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
verdicts
UNVERDICTED 4representative citing papers
Spoof-SUPERB benchmark shows large-scale discriminative SSL models such as XLS-R, UniSpeech-SAT, and WavLM Large outperform others in audio deepfake detection and maintain robustness under acoustic degradations.
Speech-FT applies drift-reduced fine-tuning followed by weight-space interpolation to improve both task performance and cross-task generalization in models such as HuBERT and WavLM.
OLIVE is a new self-supervised speech representation framework that unifies view-augmented masked latent prediction with waveform reconstruction under one objective.
citing papers explorer
-
S-JEPA : Soft Clustering Anchors for Self-Supervised Speech Representation Learning
S-JEPA uses soft GMM posteriors in a JEPA framework for self-supervised speech learning, achieving lowest WER below 90M parameters without offline re-clustering.
-
A SUPERB-Style Benchmark of Self-Supervised Speech Models for Audio Deepfake Detection
Spoof-SUPERB benchmark shows large-scale discriminative SSL models such as XLS-R, UniSpeech-SAT, and WavLM Large outperform others in audio deepfake detection and maintain robustness under acoustic degradations.
-
Speech-FT: Merging Pre-trained And Fine-Tuned Speech Representation Models For Cross-Task Generalization
Speech-FT applies drift-reduced fine-tuning followed by weight-space interpolation to improve both task performance and cross-task generalization in models such as HuBERT and WavLM.
-
OLIVE: View-Augmented Latent Prediction with Waveform Reconstruction for Speech SSL
OLIVE is a new self-supervised speech representation framework that unifies view-augmented masked latent prediction with waveform reconstruction under one objective.