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
DMLR: Data-centric Machine Learning Research -- Past, Present and Future
Luis Oala, Manil Maskey, Lilith Bat-Leah, Alicia Parrish, Nezihe Merve Gürel, Tzu-Sheng Kuo, Yang Liu, Rotem Dror, Danilo Brajovic, Xiaozhe Yao, Max Bartolo, William A Gaviria Rojas, Ryan Hileman, Rainier Aliment, Michael W. Mahoney, Meg Risdal, Matthew Lease, Wojciech Samek, Debojyoti Dutta, Curtis G Northcutt, Cody Coleman, Braden Hancock, Bernard Koch, Girmaw Abebe Tadesse, Bojan Karlaš, Ahmed Alaa, Adji Bousso Dieng, Natasha Noy, Vijay Janapa Reddi, James Zou, Praveen Paritosh, Mihaela van der Schaar, Kurt Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson
Robust Hole-Detection in Triangular Meshes Irrespective of the Presence of Singular Vertices
Mauhing Yip, Annette Stahl, Christian Schellewald
Combining Past, Present and Future: A Self-Supervised Approach for Class Incremental Learning
Xiaoshuang Chen, Zhongyi Sun, Ke Yan, Shouhong Ding, Hongtao Lu
Evaluating Robustness of Dialogue Summarization Models in the Presence of Naturally Occurring Variations
Ankita Gupta, Chulaka Gunasekara, Hui Wan, Jatin Ganhotra, Sachindra Joshi, Marina Danilevsky