Traditional approaches to automatic emotion recognition are relying on the application of handcrafted features. More recently however the advent of deep learning enabled algorithms to learn meaningful representations of input data automatically. In this paper, we investigate the applicability of transferring knowledge learned from large text and audio corpora to the task of automatic emotion recognition. To evaluate the practicability of our approach, we are taking part in this year's Interspeech ComParE Elderly Emotion Sub-Challenge, where the goal is to classify spoken narratives of elderly people with respect to the emotion of the speaker. Our results show that the learned feature representations can be effectively applied for classifying emotions from spoken language. We found the performance of the features extracted from the audio signal to be not as consistent as those that have been extracted from the transcripts. While the acoustic features achieved best in class results on the development set, when compared to the baseline systems, their performance dropped considerably on the test set of the challenge. The features extracted from the text form, however, are showing promising results on both sets and are outperforming the official baseline by 5.7 percentage points unweighted average recall.
Multimodal Emotion Recognition from Raw Audio with Sinc-convolution
Beijing JiaoTong University（北京交通大学）；Research Center of 6G Mobile Communications（6G移动通信研究中心）；School of Cyber Science and Engineering（网络空间科学与工程学院）；Wuhan National Laboratory for Optoelectronics（武汉光电国家研究中心）；Huazhong University of Science and Technology（华中科技大学）
语音控制的设备和服务在消费者物联网中已经变得非常流行。基于云的语音分析服务使用语音识别技术从语音输入中提取信息。因此，服务提供商可以建立用户的人口统计类别、个人偏好、情绪状态等的非常准确的简档，因此可能严重损害他们的隐私。为了解决这个问题，我们在用户和云服务之间开发了一个保护隐私的中间层，直接在边缘设备上对语音输入进行净化。我们使用基于CycleGAN的语音转换从原始语音输入信号中删除敏感信息，然后重新生成中和信号进行转发。我们使用相对便宜的Raspberry Pi 4来实现和评估我们的情感过滤方法，并表明性能准确性在边缘不会受到影响。事实上，在边缘处生成的信号与基于云的语音识别方法仅略有不同（约0.16%）。对生成信号的实验评估表明，对说话者情绪状态的识别可以减少约91%。
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）
Enriching Multimodal Sentiment Analysis through Textual Emotional Descriptions of Visual-Audio Content
Tianjin University（天津大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东人工智能与数字经济实验室（深圳））；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
ML-SAN: Multi-Level Speaker-Adaptive Network for Emotion Recognition in Conversations
Joint Research Laboratory for Embodied Intelligence, Xinjiang University（新疆大学具身智能联合研究实验室）；Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing, Xinjiang University（丝绸之路多语种认知计算国际联合研究实验室）；School of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）
Towards Universal End-to-End Affect Recognition from Multilingual Speech by ConvNets
The Hong Kong University of Science and Technology（香港科技大学）；Human Language Technology Center, Department of Electronic and Computer Engineering（电子与计算机工程系人类语言技术中心）
