Speech Presence
Speech presence, encompassing the accurate representation and processing of speech signals in various contexts, is a multifaceted research area aiming to improve robustness and efficiency in applications like speech synthesis, recognition, and analysis. Current research focuses on developing models that handle challenges such as noisy or incomplete data, interference from other sources (e.g., background noise or multiple speakers), and the presence of biases or spurious correlations in training data, often employing deep learning architectures and causal inference methods. These advancements are crucial for creating more reliable and adaptable systems across diverse applications, ranging from improved human-computer interaction to more accurate medical diagnosis and environmental monitoring.
Papers
Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions
Jean Feng, Alexej Gossmann, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio
Cross-inhibition leads to group consensus despite the presence of strongly opinionated minorities and asocial behaviour
Andreagiovanni Reina, Raina Zakir, Giulia De Masi, Eliseo Ferrante