Untranscribed Data

Untranscribed data, encompassing various modalities like audio and images, presents significant challenges and opportunities for machine learning. Current research focuses on developing robust methods to leverage this data for tasks such as speech synthesis, 3D animation, and classification, often employing diffusion models, Bayesian approaches, and generative classifiers to handle noise and uncertainty. These efforts aim to improve model performance and reduce reliance on large, meticulously curated datasets, thereby broadening the applicability of machine learning to diverse and less-structured real-world scenarios. The resulting advancements have implications for personalized technologies and efficient data utilization across numerous fields.