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Found 5 papers, 2 papers with code
An Efficient End-to-End Approach to Noise Invariant Speech Features via Multi-Task Learning
Lastly, we show that the proposed recipe can be applied to other distillation methodologies, such as the recent DPWavLM.
MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees
We then provide an overview of the phenotypic data distribution as well as a visualization of the sensor data patterns.
VIC-KD: Variance-Invariance-Covariance Knowledge Distillation to Make Keyword Spotting More Robust Against Adversarial Attacks
Keyword spotting (KWS) refers to the task of identifying a set of predefined words in audio streams.
RobustDistiller: Compressing Universal Speech Representations for Enhanced Environment Robustness
The proposed layer-wise distillation recipe is evaluated on top of three well-established universal representations, as well as with three downstream tasks.
Improving the Robustness of DistilHuBERT to Unseen Noisy Conditions via Data Augmentation, Curriculum Learning, and Multi-Task Enhancement
Self-supervised speech representation learning aims to extract meaningful factors from the speech signal that can later be used across different downstream tasks, such as speech and/or emotion recognition.
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