Search Results for author:
Found 3 papers, 2 papers with code
Compressing Transformer-based self-supervised models for speech processing
Despite the success of Transformers in self- supervised learning with applications to various downstream tasks, the computational cost of training and inference remains a major challenge for applying these models to a wide spectrum of devices.
MelHuBERT: A simplified HuBERT on Mel spectrograms
Self-supervised models have had great success in learning speech representations that can generalize to various downstream tasks.
SUPERB @ SLT 2022: Challenge on Generalization and Efficiency of Self-Supervised Speech Representation Learning
We present the SUPERB challenge at SLT 2022, which aims at learning self-supervised speech representation for better performance, generalization, and efficiency.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
