On the disentanglement and robustness of self-supervised speech representations Universiteit Gent
This paper conducts an analysis of latent embeddings generated by a range of pre-trained, self-supervised learning (SSL) models. Departing from conventional practices that predominantly focus on examining these embeddings within the realm of speech recognition tasks, our study investigates the characteristics associated with speakers and their behavior under the influence of input distortions. We establish a controlled setting with varying ...