Automated speaking assessment (ASA) typically involves automatic speech recognition (ASR) and hand-crafted feature extraction from the ASR transcript of a learner's speech. Recently, self-supervised learning (SSL) has shown stellar performance compared to traditional methods. However, SSL-based ASA systems are faced with at least three data-related challenges: limited annotated data, uneven distribution of learner proficiency levels and non-uniform score intervals between different CEFR proficiency levels. To address these challenges, we explore the use of two novel modeling strategies: metric-based classification and loss reweighting, leveraging distinct SSL-based embedding features. Extensive experimental results on the ICNALE benchmark dataset suggest that our approach can outperform existing strong baselines by a sizable margin, achieving a significant improvement of more than 10% in CEFR prediction accuracy.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection
Informatics Institute, University of Amsterdam（阿姆斯特丹大学信息学院）；Department of Computer Science and Technology, SUNY Empire State University（SUNY埃默里州立大学计算机科学与技术系）
Enhancing Indonesian Automatic Speech Recognition: Evaluating Multilingual Models with Diverse Speech Variabilities
Institut Teknologi Bandung（万隆理工学院）；University of Indonesia（印度尼西亚大学）；Japan Advanced Institute of Science and Technology（日本先进科学技术学院）；Nara Institute of Science and Technology（奈良科学技术大学）
Improving End-to-End Speech Recognition for Dysarthric Speech through In-Domain Data Augmentation
Department of Electronics and Communication Engineering, National Institute of Technology Sikkim, India（电子与通信工程系，印度尼特拉特技术学院Sikkim分校）；Signal Analysis and Interpretation Laboratory (SAIL), University of Southern California, Los Angeles, USA（信号分析与解释实验室（SAIL），美国南加州大学洛杉矶分校）
An Inclusive Foundation Model for Generalizable Cytogenetics in Precision Oncology
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属新华医院）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction
School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院）；College of Physics, Nanjing University of Aeronautics and Astronautics（南京航空航天大学物理学院）；Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong-Shenzhen（香港中文大学（深圳）大数据研究院）；Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学电磁频谱空间动态认知系统重点实验室）
Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learning
Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；LORIA（洛林计算机科学与应用数学研究所）；Université Paul-Valéry Montpellier（蒙彼利埃保罗-瓦莱里大学）；Praxiling（普拉西林实验室）
