Data Efficiency and Privacy Preservation for Personalized Machine Learning Models: from the Perspective of Audio Applications
Minje Kim is an assistant professor in the Dept. of Intelligent Systems Engineering at Indiana University, where he leads his research group, Signals and AI Group in Engineering (SAIGE). He is also an Amazon Visiting Academic, consulting for Amazon Lab126. At IU, he is affiliated with various programs and labs such as Data Science, Cognitive Science, Dept. of Statistics, and Center for Machine Learning. He earned his Ph.D. in the Dept. of Computer Science at the University of Illinois at Urbana-Champaign. Before joining UIUC, He worked as a researcher at ETRI, a national lab in Korea, from 2006 to 2011. Before then, he received his Master’s and Bachelor’s degrees in the Dept. of Computer Science and Engineering at POSTECH (Summa Cum Laude) and in the Division of Information and Computer Engineering at Ajou University (with honor) in 2006 and 2004, respectively. He is a recipient of various awards including NSF Career Award (2021), IU Trustees Teaching Award (2021), IEEE SPS Best Paper Award (2020), and Google and Starkey’s grants for outstanding student papers in ICASSP 2013 and 2014, respectively. He is an IEEE Senior Member and also a member of the IEEE Audio and Acoustic Signal Processing Technical Committee (2018-2023). He is serving as an Associate Editor for EURASIP Journal of Audio, Speech, and Music Processing, and as a Consulting Associate Editor for IEEE Open Journal of Signal Processing. He is also a reviewer, program committee member, or area chair for the major machine learning and signal processing venues. He is on more than 50 patents as an inventor.
One of the keys to success in machine learning applications is to improve each user’s personal experience via personalized models. A personalized model can be a more resource-efficient solution than a general-purpose model, too, because it focuses on a particular sub-problem, for which a smaller model architecture can be good enough. However, training a personalized model requires data from the particular test-time user, which are not always available due to their private nature. Furthermore, such data tend to be unlabeled as they can be collected only during the test time, once after the system is deployed to user devices. One could rely on the generalization power of a generic model, but such a model can be too computationally/spatially complex for real-time processing in a resource-constrained device. In this talk, I will present some techniques to circumvent the lack of labeled personal data in the context of speech enhancement. Our machine learning models will require zero or few data samples from the test-time users, while they can still achieve the personalization goal. To this end, we will investigate modularized speech enhancement models as well as the potential of adversarial optimization and self-supervised learning for zero- or few-shot fine-tuning for personalized speech enhancement. Because our research achieves the personalization goal in a privacy-preserving and resource-efficient way, it is a step towards a more available and affordable AI for society, while modern AI tends to be in the form of a large-scale generalist, which sometimes makes the model underperform for the socially under-represented groups.