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Found 6 papers, 1 papers with code
A Large-Scale Evaluation of Speech Foundation Models
In this work, we establish the Speech processing Universal PERformance Benchmark (SUPERB) to study the effectiveness of the paradigm for speech.
Investigating Human-Identifiable Features Hidden in Adversarial Perturbations
Our study contributes to a deeper understanding of the underlying mechanisms behind adversarial attacks and offers insights for the development of more resilient defense strategies for neural networks.
Ensemble knowledge distillation of self-supervised speech models
We tried two different aggregation techniques, layerwise-average and layerwise-concatenation, to the representations of different teacher models and found that the former was more effective.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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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.
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.
Searching for the Essence of Adversarial Perturbations
Neural networks have demonstrated state-of-the-art performance in various machine learning fields.
