Research Projects

The project aims to create mathematical tools to measure and evaluate the transferability of deep neural network (DNN)-based models for specific tasks. It will use time-frequency and topology-based methods to create an empirically easy-to-compute metric for assessing pre-trained acoustic models. The metric will analyze the trajectory growth of geometric objects passing through DNNs to link expressivity and generalization error in these models. The project also aims to study how these models can integrate spectral and/or temporal task-dependent information. The methods will be analyzed theoretically and empirically for various pre-trained models, downstream tasks, and modalities.