30 citations · 44 across the 12 of their papers we have counts for
4 papers · 1 filter
Learning Disentangled Speech Representations
Yusuf Brima, Ulf Krumnack, Simone Pika +1
Disentangled representation learning in speech processing has lagged behind other domains, largely due to the lack of datasets with annotated generative factors for robust evaluati…
Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction
Yusuf Brima, Ulf Krumnack, Simone Pika +1
Self-supervised learning (SSL) has emerged as a promising paradigm for learning flexible speech representations from unlabeled data. By designing pretext tasks that exploit statist…
Investigating Pre-trained Language Models on Cross-Domain Datasets, a Step Closer to General AI
Mohamad Ballout, Ulf Krumnack, Gunther Heidemann +1
Pre-trained language models have recently emerged as a powerful tool for fine-tuning a variety of language tasks. Ideally, when models are pre-trained on large amount of data, they…
Opening the Black Box: Analyzing Attention Weights and Hidden States in Pre-trained Language Models for Non-language Tasks
Mohamad Ballout, Ulf Krumnack, Gunther Heidemann +1
Investigating deep learning language models has always been a significant research area due to the ``black box" nature of most advanced models. With the recent advancements in pre-…