Geometric Neuroevolution for Fine-tuning Automatic Speech Recognition

This project aims to develop a geometry-aware neuroevolution framework by integrating information geometry into evolutionary algorithms for neural network training. By leveraging the geometry of the neuromanifold, we seek to enhance optimization efficiency and overcome challenges like slow and premature convergence. We will apply our optimization techniques to automatic
speech recognition, improving the fine-tuning of foundation models. In collaboration with the Freie Universität Berlin library, we
will enhance transcription accuracy for audiovisual research resources, reducing errors in entity recognition and strengthening
the reliability of automatic speech recognition.