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
Reliable Multi-Object Tracking in the Presence of Unreliable Detections
Travis Mandel, Mark Jimenez, Emily Risley, Taishi Nammoto, Rebekka Williams, Max Panoff, Meynard Ballesteros, Bobbie Suarez
Guaranteed Nonlinear Tracking in the Presence of DNN-Learned Dynamics With Contraction Metrics and Disturbance Estimation
Pan Zhao, Ziyao Guo, Aditya Gahlawat, Hyungsoo Kang, Naira Hovakimyan
Channel Parameter Estimation in the Presence of Phase Noise Based on Maximum Correntropy Criterion
Amir Alizadeh, Ghosheh Abed Hodtani