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
A Study on the Impact of Data Augmentation for Training Convolutional Neural Networks in the Presence of Noisy Labels
Emeson Santana, Gustavo Carneiro, Filipe R. Cordeiro
Strategic Decision-Making in the Presence of Information Asymmetry: Provably Efficient RL with Algorithmic Instruments
Mengxin Yu, Zhuoran Yang, Jianqing Fan