Name: Tali Dror Name: Shani Budilovsky Name: Shalev Haimovich

Name: Tali Dror Degree: MSc in Electrical Engineering Supervisor: Prof. Haim Permuter Title: Reconstructing High-Resolution Faces from Speech Using Speaker-Aligned Diffusion Models Humans can infer aspects of a person’s facial identity from their voice alone, suggesting that speech and appearance share underlying anatomical and physiological cues. Learning this relationship computationally, however, is challenging due to the ambiguity of voice-to-face mapping, limited paired data, and the difficulty of preserving identity in realistic face generation. In this work we present a speech-to-face synthesis framework that addresses these challenges by combining identity-aware speaker representation learning with a pretrained diffusion-based face generator. By aligning speech and face embeddings in a shared identity space and carefully conditioning a diffusion model under limited supervision, our approach achieves significantly improved identity fidelity. Speaker: Shani Budilovsky M.Sc. student in Communication Systems Engineering, Electrical and Computer Engineering School, Ben-Gurion University of the Negev. Titel: Data Augmentation for Spoofing-robust Automatic Speaker Verification Supervisors: Dr. Yehuda Ben-Shimol and Prof. Itshak Lapidot Abstract: Automatic Speaker Verification (ASV) is a biometric authentication technology that verifies a speaker’s identity based on their voice characteristics and is widely used in security-sensitive applications such as access control and identity verification. Despite their success, ASV systems are vulnerable to spoofing attacks, including replay, synthetic, and manipulated speech. To address this challenge, Spoofing-robust ASV (SASV) systems combine speaker verification models with dedicated spoofing countermeasures to jointly improve verification accuracy and attack resistance. Data augmentation has become a key technique for improving the generalization and robustness of deep learning-based ASV and countermeasure models by increasing training data diversity and simulating real-world acoustic variability. Augmentation methods can be applied at the waveform level or at the time-frequency representation level, such as spectrogram-based techniques. However, in the SASV setting, not all augmentation strategies are equally beneficial, and some may improve speaker discrimination while degrading spoof detection performance, or vice versa. This seminar examines the effect of different data augmentation strategies on both ASV and spoofing-robust ASV systems, and discusses the importance of carefully designing augmentation pipelines that balance speaker verification accuracy with spoofing robustness in practical SASV systems. Student name: Shalev Haimovich Advisor : Matan Gal-Katziri M.Sc. in Electrical Engineering Seminar subject: A CMOS-Ready 1064 nm TFLN PIC for 3D Heterogeneous Integration. Abstract: Photonic integrated circuits (PICs) offer a promising path toward high-bandwidth and energy-efficient systems, yet active photonic devices are typically controlled, driven, and interfaced with electronic circuits. This seminar presents the design of a CMOS-ready Silicon Nitride-loaded Thin-Film Lithium Niobate PIC for electro-optic phase modulation at 1064 nm, developed toward a future 3D heterogeneous multi-chip module. Instead of treating the modulator as a standalone optical device, the design considers the PIC as both the photonic layer and part of the RF interface of the integrated system. The seminar will discuss how the optical platform, traveling-wave electrodes, RF input path, impedance and termination strategy, electromagnetic modeling, and CMOS output-stage constraints are brought together to support a practical CMOS–PIC integration scheme.