In speech emotion recognition tasks, models learn emotional representations from datasets. We find the data distribution in the IEMOCAP dataset is very imbalanced, which may harm models to learn a better representation. To address this issue, we propose a novel Pairwise-emotion Data Distribution Smoothing (PDDS) method. PDDS considers that the distribution of emotional data should be smooth in reality, then applies Gaussian smoothing to emotion-pairs for constructing a new training set with a smoother distribution. The required new data are complemented using the mixup augmentation. As PDDS is model and modality agnostic, it is evaluated with three SOTA models on the IEMOCAP dataset. The experimental results show that these models are improved by 0.2\% - 4.8\% and 1.5\% - 5.9\% in terms of WA and UA. In addition, an ablation study demonstrates that the key advantage of PDDS is the reasonable data distribution rather than a simple data augmentation.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices
Hybrid Data Augmentation and Deep Attention-based Dilated Convolutional-Recurrent Neural Networks for Speech Emotion Recognition
Institute for Computational Science, Ton Duc Thang University（孙德胜大学计算科学研究所）；Faculty of Electrical and Electronics Engineering, Ton Duc Thang University（孙德胜大学电气与电子工程学院）；School of Graduate Studies, Ton Duc Thang University（孙德胜大学研究生院）
Speech Emotion Recognition Using Deep Sparse Auto-Encoder Extreme Learning Machine with a New Weighting Scheme and Spectro-Temporal Features Along with Classical Feature Selection and A New Quantum-Inspired Dimension Reduction Method
Crab: Multi Layer Contrastive Supervision to Improve Speech Emotion Recognition Under Both Acted and Natural Speech Condition
Dept. of Computer Engineering and Automation (DCA), Faculdade de Engenharia Elétrica e de Computação（计算机工程与自动化系（DCA）、电气与计算机工程学院）；AI Lab.（人工智能实验室）；Institute of Computing, Universidade Estadual de Campinas, UNICAMP（计算学院，坎皮纳斯州立大学，UNICAMP）
Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations
Central South University of Forestry and Technology（中南林业科技大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；State University of New York, New Paltz（纽约州立大学新帕尔茨分校）
Sync-TVA: A Graph-Attention Framework for Multimodal Emotion Recognition with Cross-Modal Fusion
James Watt School of Engineering, University of Glasgow（格拉斯哥大学詹姆斯·瓦特工程学院）；University of Bristol（布里斯托大学）；School of Engineering Mathematics and Technology, University of Bristol（布里斯托大学工程数学与技术学院）；School of Computing Science, University of Glasgow（格拉斯哥大学计算科学学院）；Department of Civil, Environmental & Geomatic Engineering, University College London (UCL)（伦敦大学学院（UCL）土木、环境与测绘工程系）
EMelodyGen: Emotion-Conditioned Melody Generation in ABC Notation with the Musical Feature Template
Department of Music AI and Information Technology, Central Conservatory of Music（中央音乐学院音乐人工智能与信息科技系）；School of Computer Science and Technology, Fudan University（复旦大学计算机科学与技术学院）；Shanghai Key Laboratory of Intelligent Information Processing, Fudan University（复旦大学智能信息处理上海重点实验室）
A Study on the Ambiguity in Human Annotation of German Oral History Interviews for Perceived Emotion Recognition and Sentiment Analysis
Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS)（弗劳恩霍夫智能分析与信息系统研究所）；Haus der Geschichte der Bundesrepublik Deutschland Foundation (HdG)（德意志联邦共和国历史之家基金会）；University of Applied Sciences Bonn-Rhein-Sieg（波恩-莱茵-锡格应用技术大学）；Autonomous Intelligent Systems (AIS), Computer Science Institute VI, Univ. of Bonn（波恩大学计算机科学第六研究所自主智能系统）
Audio-Guided Fusion Techniques for Multimodal Emotion Analysis
Beijing University of Posts and Telecommunications（北京邮电大学）；State Key Laboratory of Networking and Switching Technology（网络与交换技术国家重点实验室）；School of Cyberspace Security（网络空间安全学院）
Relational graph-driven differential denoising and diffusion attention fusion for multimodal conversation emotion recognition
College of Computer and Mathematics, Central South University of Forestry and Technology（中南林业科技大学计算机与数学学院）；Department of Computer Science, State University of New York, New Paltz（纽约州立大学新帕尔茨分校计算机科学系）
