Chen Xiang, Huang Rubing, Li Xin, Xiao Lei, Zhou Ming, Zhang Linghao
School of Design, Jiangnan University, Wuxi, China.
Faculty of Information Technology, Macau University of Science and Technology, Macau, China.
Front Psychol. 2021 Apr 20;12:674853. doi: 10.3389/fpsyg.2021.674853. eCollection 2021.
Emotional design is an important development trend of interaction design. Emotional design in products plays a key role in enhancing user experience and inducing user emotional resonance. In recent years, based on the user's emotional experience, the design concept of strengthening product emotional design has become a new direction for most designers to improve their design thinking. In the emotional interaction design, the machine needs to capture the user's key information in real time, recognize the user's emotional state, and use a variety of clues to finally determine the appropriate user model. Based on this background, this research uses a deep learning mechanism for more accurate and effective emotion recognition, thereby optimizing the design of the interactive system and improving the user experience. First of all, this research discusses how to use user characteristics such as speech, facial expression, video, heartbeat, etc., to make machines more accurately recognize human emotions. Through the analysis of various characteristics, the speech is selected as the experimental material. Second, a speech-based emotion recognition method is proposed. The mel-Frequency cepstral coefficient (MFCC) of the speech signal is used as the input of the improved long and short-term memory network (ILSTM). To ensure the integrity of the information and the accuracy of the output at the next moment, ILSTM makes peephole connections in the forget gate and input gate of LSTM, and adds the unit state as input data to the threshold layer. The emotional features obtained by ILSTM are input into the attention layer, and the self-attention mechanism is used to calculate the weight of each frame of speech signal. The speech features with higher weights are used to distinguish different emotions and complete the emotion recognition of the speech signal. Experiments on the EMO-DB and CASIA datasets verify the effectiveness of the model for emotion recognition. Finally, the feasibility of emotional interaction system design is discussed.
情感设计是交互设计的一个重要发展趋势。产品中的情感设计在提升用户体验和引发用户情感共鸣方面起着关键作用。近年来，基于用户的情感体验，强化产品情感设计的设计理念已成为大多数设计师改进设计思维的新方向。在情感交互设计中，机器需要实时捕捉用户的关键信息，识别用户的情感状态，并利用各种线索最终确定合适的用户模型。基于此背景，本研究采用深度学习机制进行更准确有效的情感识别，从而优化交互系统设计并提升用户体验。首先，本研究探讨如何利用语音、面部表情、视频、心跳等用户特征，使机器更准确地识别人类情感。通过对各种特征的分析，选择语音作为实验材料。其次，提出了一种基于语音的情感识别方法。语音信号的梅尔频率倒谱系数（MFCC）被用作改进的长短时记忆网络（ILSTM）的输入。为确保信息的完整性和下一时刻输出的准确性，ILSTM在LSTM的遗忘门和输入门中进行窥视孔连接，并将单元状态作为输入数据添加到阈值层。ILSTM获得的情感特征被输入到注意力层，利用自注意力机制计算语音信号每一帧的权重。权重较高的语音特征用于区分不同情感，完成语音信号的情感识别。在EMO-DB和CASIA数据集上的实验验证了该模型用于情感识别的有效性。最后，讨论了情感交互系统设计的可行性。
