Marta Moscati — accepted AI-conference papers

2026

Face-Voice Association with Inductive Bias for Maximum Class Separation

Marta Moscati, Mubashir Noman, Yufang Hou, Markus Schedl

ICASSP 2026oral

Face-voice association is widely studied in multimodal learning and is approached representing faces and voices with embeddings that are close for a same person and well separated from those of others. Previous work achieved this with loss functions. Recent advancements in classification have shown…

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2026

Linking Faces and Voices Across Languages: Insights from the FAME 2026 Challenge

Marta Moscati, Ahmed Abdullah, Muhammad Saad Saeed, Shah Nawaz, Rohan Kumar Das, Muhammad Zaigham Zaheer +3

ICASSP 2026poster

Over half of the world's population is bilingual and people often communicate under multilingual scenarios. The Face-Voice Association in Multilingual Environments (FAME) 2026 Challenge, held at ICASSP 2026, focuses on developing methods for face-voice association that are effective when the languag…

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