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Found 7 papers, 1 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.
On the Impact of Quantization and Pruning of Self-Supervised Speech Models for Downstream Speech Recognition Tasks "In-the-Wild''
Recent advances with self-supervised learning have allowed speech recognition systems to achieve state-of-the-art (SOTA) word error rates (WER) while requiring only a fraction of the labeled training data needed by its predecessors.
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.
On the Transferability of Whisper-based Representations for "In-the-Wild" Cross-Task Downstream Speech Applications
Large self-supervised pre-trained speech models have achieved remarkable success across various speech-processing tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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An Exploration into the Performance of Unsupervised Cross-Task Speech Representations for "In the Wild'' Edge Applications
Later, these representations serve as input to downstream models to solve a number of tasks, such as keyword spotting or emotion recognition.
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.
