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
The Expanding Scope of the Stability Gap: Unveiling its Presence in Joint Incremental Learning of Homogeneous Tasks
Sandesh Kamath, Albin Soutif-Cormerais, Joost van de Weijer, Bogdan Raducanu
Robustness Assessment of Mathematical Reasoning in the Presence of Missing and Contradictory Conditions
Shi-Yu Tian, Zhi Zhou, Lin-Han Jia, Lan-Zhe Guo, Yu-Feng Li