AI-Evaluation

Background Speech enhancement, the process of improving the quality speech signals, can not only improve quality of experience for listeners and the quality of communication, it can also aid the performance of machine-and deep-learning models in downstream tasks. However, the challenge of the trade-off between noise removal and artifact incorporation is ongoing [1]. The project aims to investigate the factors influencing noise reduction preferences and develop a technical framework around it. Low data-resources will be an important consideration in this project. ...

Background We are seeking students interested advancing speech technology in low-resource environments. The project is sufficiently open-ended and will be focused on developing machine learning models and algorithms tailored to address the unique challenges posed by limited data and computational resources in speech processing, also in high-stakes applications like healthcare and education. Objective(s) Potential directions are: Research and develop novel machine learning techniques optimized for low-resource speech technology applications. Design and implement efficient algorithms for speech recognition, synthesis, and understanding in resource-constrained settings. Conduct experiments, analyze results, and iterate on models to continuously improve performance and robustness. Contribute to the development of tools and frameworks to streamline the deployment and evaluation of low-resource speech models. Requirements Need to have: ...