REVIEW 3 cited by
Speech SIMCLR: Combining Contrastive and Reconstruction Objective for Self-supervised Speech Representation Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Self-supervised visual pretraining has shown significant progress recently. Among those methods, SimCLR greatly advanced the state of the art in self-supervised and semi-supervised learning on ImageNet. The input feature representations for speech and visual tasks are both continuous, so it is natural to consider applying similar objective on speech representation learning. In this paper, we propose Speech SimCLR, a new self-supervised objective for speech representation learning. During training, Speech SimCLR applies augmentation on raw speech and its spectrogram. Its objective is the combination of contrastive loss that maximizes agreement between differently augmented samples in the latent space and reconstruction loss of input representation. The proposed method achieved competitive results on speech emotion recognition and speech recognition.
Forward citations
Cited by 3 Pith papers
-
Latent Space Consistency for Sparse-View CT Reconstruction
CLS-DM adds a contrastive-learning alignment stage and a reconstruction constraint to a latent diffusion model for sparse-view 3D CT reconstruction.
-
Delayed Bidirectional Alignment via Disentangled Audio Semantics for Audio-Visual Segmentation
A delayed bidirectional audio-visual alignment framework with bank-grounded disentangled audio queries achieves state-of-the-art segmentation results on AVS-Objects and VPO benchmarks.
-
Balancing Information Preservation and Disentanglement in Self-Supervised Music Representation Learning
A multi-view SSL framework with combined reconstruction and separation-based contrastive losses obtains disentangled pitch and instrument subspaces without the accuracy loss seen with contrastive-only training on NSynth.
Discussion (0). Sign in to comment.