4 accepted papers
Fine-tuning foundation models pre-trained on large-scale data is a promising approach for low-resource scenarios. However, the performance is heavily dependent on the amount of pre-training data available for the target language, and fine-tuning with limited data can lead to overfitting and may have…
Speech Emotion Recognition (SER) can play an important role in human-computer interaction. In this paper, we propose a logit knowledge distillation method for SER, called Cubic KD, that distill the knowledge of fine-tuned self-supervised models to allow better performance of small models. By creatin…
Fine-tuning of self-supervised models is a powerful transfer learning method in a variety of fields, including speech processing, since it can utilize generic feature representations obtained from large amounts of unlabeled data. Fine-tuning, however, requires a new parameter set for each downstream…
We propose an algorithm to automatically restrict the step size in the iterative optimization process with an application to adversarial attacks on speaker verification models. The proposed algorithm dynamically determines a subspace with a restriction radius r to which the Taylor approximation is a…