Abstract

Our research focuses on improving speech processing algorithms, such as automatic speech recognition (ASR), speaker identification, and depression detection, under challenging conditions such as limited data (for example, children’s or clinical speech), mismatched conditions (for example, training on read speech while recognizing conversational speech), and noisy speech, using a hybrid data-driven and knowledge-based approach. This approach requires understanding of both machine learning approaches and of the human speech production and perception systems. I will summarize in this talk our work on children’s ASR using self-supervised models, detecting depression from speech signals using novel speaker disentaglement techniques, and automating scoring of children’s reading tasks with both ASR and innovative NLP algorithms.

Biography

Abeer Alwan received her Ph.D. in Electrical Engineering and Computer Science from MIT in 1992. Since then, she has been with the ECE department at UCLA where she is now a Full Professor and directs the Speech Processing and Auditory Perception Laboratory. She is the recipient of the NSF Research Initiation and Career Awards, NIH FIRST Award, UCLA-TRW Excellence in Teaching Award, Okawa Foundation Award in Telecommunication, and the Engineer’s Council Educator Award. She is a Fellow of the Acoustical Society of America, IEEE, and International Speech Communication Assoc. (ISCA). She was a Fellow at the Radcliffe Institute, Harvard University, co-Editor in Chief of Speech Communication, Associate Editor of JASA and IEEE TSALP, a Distinguished Lecturer of ISCA, a member of the IEEE Signal Processing Board of Governers and she is currently on the advisory board of ISCA and the UCLA-Amazon Science Hub for Humanity and AI.