Emotions are an integral part of human interactions and are significant factors in determining user satisfaction or customer opinion. speech emotion recognition (SER) modules also play an important role in the development of human–computer interaction (HCI) applications. A tremendous number of SER systems have been developed over the last decades. Attention-based deep neural networks (DNNs) have been shown as suitable tools for mining information that is unevenly time distributed in multimedia content. The attention mechanism has been recently incorporated in DNN architectures to emphasise also emotional salient information. This paper provides a review of the recent development in SER and also examines the impact of various attention mechanisms on SER performance. Overall comparison of the system accuracies is performed on a widely used IEMOCAP benchmark database.

中文翻译：

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基于深度学习和注意力机制的语音情感识别研究述评

情绪是人类互动的组成部分，是决定用户满意度或客户意见的重要因素。语音情感识别（SER）模块在人机交互（HCI）应用程序的开发中也起着重要作用。在过去的几十年中，已经开发了许多SER系统。基于注意力的深度神经网络（DNN）已显示为用于挖掘多媒体内容中时间分布不均匀的信息的合适工具。注意机制最近已被并入DNN架构中，以强调情感上的重要信息。本文概述了SER的最新发展，并研究了各种注意机制对SER性能的影响。