Human emotion recognition by optimally fusing facial expression and speech feature

Emotion recognition is a hot research in modern intelligent systems. The technique is pervasively used in autonomous vehicles, remote medical service, and human–computer interaction (HCI). Traditional speech emotion recognition algorithms cannot be effectively generalized since both training and testing data are from the same domain, which have the same data distribution. In practice, however, speech data is acquired from different devices and recording environments. Thus, the data may differ significantly in terms of language, emotional types and tags. To solve such problem, in this work, we propose a bimodal fusion algorithm to realize speech emotion recognition, where both facial expression and speech information are optimally fused. We first combine the CNN and RNN to achieve facial emotion recognition. Subsequently, we leverage the MFCC to convert speech signal to images. Therefore, we can leverage the LSTM and CNN to recognize speech emotion. Finally, we utilize the weighted decision fusion method to fuse facial expression and speech signal to achieve speech emotion recognition. Comprehensive experimental results have demonstrated that, compared with the uni-modal emotion recognition, bimodal features-based emotion recognition achieves a better performance.     

Hybrid chaos optimization algorithm with artificial emotion

Much research on Artificial Intelligence (AI) has been focusing on exploring various potential applications of intelligent systems. In most cases, the researches attempt to model human intelligence by mimicking the brain structure and function, but they ignore an important aspect in human learning and decision making: the artificial emotion. In this paper, we present a new unconstrained global optimization method, hybrid chaos optimization algorithm with artificial emotion (HCOAAE), which avoids trapping to local minima, and improves convergence in large space and high-dimension optimization problems. The main purpose of artificial emotion is to mimic decision making behavior process of humans, to choose most suitable parameters of HCOAAE and decide whether to change current search strategy or not in the next iteration. Numerical simulations of 13 benchmark functions with different dimensions are used to test the performance of HCOAAE. Experimental results show that the proposed method significantly outperforms the existing methods in terms of convergence speed, computational effectiveness, and numerical stability.     

The Multi-Weight Neuron with Geometry Algorithm and Its Application

A novel approach for multi-dimension signals processing, that is multi-weight neural network based on high dimensional geometry theory, is proposed. With this theory, the geometry algorithm for building the multiweight neuron is mentioned. To illustrate the advantage of the novel approach, a Chinese speech emotion recognition experiment has been done. From this experiment, the human emotions are classified into 6 archetypal classes： fear, anger, happiness, sadness, surprise and disgust. And the amplitude, pitch frequency and formant are used as the feature parameters for speech emotion recognition. Compared with traditional GSVM model, the new method has its superiority. It is noted that this method has significant values for researches and applications henceforth.     

恶意欠薪问题的定量分析

在我国,拖欠工资现象仍广泛存在于某些行业中。它引发了被拖欠者的负面情绪,进而带来了种种社会问题,政府部门自然要干预其中。基于欠薪的动态决策过程,本文在劳动者负面情绪阈值的两种分布下,分别讨论了企业的欠薪行为,其结果非常直观地显示了企业恶意欠薪的根源在于“有利可图”。文章从量化角度明确了增强劳动者的维权意识是治理欠薪的有效途径,而政府干预是劳动者维权行为的必要保证。此外,建立随机检查机制可以让政府干预在治理欠薪中发挥出实际效果,政府也可以通过调控经济环境达到治理欠薪的目的。     

网站特性对消费者在线冲动购买的影响

基于不同行业和地区319名有网络购物经验消费者的问卷调查数据和S-O-R理论,研究了网站的视觉吸引力及易用性对情绪和冲动购买的影响.结果发现:1)网站的视觉吸引力和易用性正向预测情绪的愉悦和唤醒度;2)愉悦正向预测冲动购买并调节唤醒与冲动购买的关系,愉悦度越高,冲动购买越多;在愉悦的情况下,唤醒度越高,冲动购买越多;在不愉悦的情况下,唤醒度越高,冲动购买越少;3)网站的视觉吸引力和易用性通过愉悦的中介作用影响冲动购买.通过模拟购物网站204名有网络购物经验女性的研究数据,验证了上述结果的稳定性.研究结果提示:网站特性在在线冲动购买中起重要作用,商家可以通过提升网站页面(如美观度和导航便利度等)促进网络销售.     

基于模糊认知图的机器人情感响应模型

针对现有人机交互系统广泛存在机器人情感响应缺乏独特性和主动性、参与人参与度、满意度、体验感不高的问题,该文依据愉悦度-激活度-优势度(PAD)情感空间提出一种基于模糊认知图的机器人情感响应模型.首先,获取参与人交互输入情感值,对其评估得到参与人的情感状态矩阵;其次,考虑到机器人的性格特征和社会角色,利用模糊认知图的时间...     

语音信号的情感特征分析与识别研究综述

语音情感的分析与识别是近年来新兴研究课题之一，本文介绍了近几年来国内外语音情感识别的状况，阐述了各种人类情感分类的方法，归纳了各种语音特征参数的提取方法以及各特征参数对情感识别的意义，在此基础上综述了国内外在情感识别领域的研究进展与主要识别建模方法，同时总结了各种识别建模方法的利弊。最后概括了语音情感识别领域的发展趋势，并进行了展望。     

语音情感识别中的特征选择方法

为了解决传统卷积神经网络在识别中文语音时预测错误率较高、泛化性能弱的问题,首先以深度卷积神经网络(DCNN)-连接时序分类(CTC)为研究对象,深入分析了不同卷积层、池化层以及全连接层的组合对其性能的影响;其次,在上述模型的基础上,提出了多路卷积神经网络(MCNN)-连接时序分类(CTC),并联合SENet提出了深度SE-MCNN-CTC声学模型,该模型融合了MCNN与SENet的优势,既能加强卷积神经网络的深层信息的传递、避免梯度问题,又可以对提取的特征图进行自适应重标定。最终实验结果表明:SE-MCNN-CTC相较于DCNN-CTC错误率相对降低13.51%,模型最终的错误率达22.21%;算法改进后的声学模型可以有效地提升泛化性能。     

游客情感计算的文本大数据挖掘方法比较研究

旅游文本大数据以其方便、快捷和低门槛的特点为游客情感计算提供了极大便利,已经成为旅游大数据的主要来源之一。基于大数据理论和情感理论,以文本大数据为数据源,在全面梳理国内外情感计算相关成果的基础上,利用人工智能中的逻辑/算法编程方法、机器学习方法、深度学习方法对旅游文本大数据进行挖掘,探索最佳的基于文本大数据的游客情感计算方法。研究发现：（1）基于情感词典的游客情感计算模型,其核心是构建情感词典和设计情感计算规则,方法简单,容易实现,适用语料范围广。（2）机器学习,用统计学方法抽取文本中的特征项,具有非线性特征,可靠性较线性特征的情感词典方法高。（3）基于深度学习技术的游客情感计算,效果良好,准确率在85%以上。训练多领域的文本语料易于移植,实用性强,且泛化能力好,较适合大数据时代游客情感计算研究。     

利用模糊熵进行参数有效性分析的语音情感识别

本文利用模糊熵理论来分析语音信号情感特征参数相对于识别情感模式的不确定度，并提出了一种利用模糊熵对情感参数有效性进行度量的方法。并将参数有效性分析结合模糊综合判别对情感语音信号作情感识别，取得了较好效果。