Zhen-Tao Liu

An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification

Classification is an important task of machine intelligence in the field of information. The artificial neural network (ANN) is widely used for classification. However, the traditional ANN shows slow training speed, and it is hard to meet the real-time requirement for large-scale applications. In this paper, an improved brain-inspired emotional learning (BEL) algorithm is proposed for fast classification. The BEL algorithm was put forward to mimic the high speed of the emotional learning mechanism in mammalian brain, which has the superior features of fast learning and low computational complexity. To improve the accuracy of BEL in classification, the genetic algorithm (GA) is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in the BEL neural network. The combinational algorithm named as GA-BEL has been tested on eight University of California at Irvine (UCI) datasets and two well-known databases (Japanese Female Facial Expression, Cohn–Kanade). The comparisons of experiments indicate that the proposed GA-BEL is more accurate than the original BEL algorithm, and it is much faster than the traditional algorithm.

Speech emotion recognition based on feature selection and extreme learning machine decision tree

Abstract Feature selection is a crucial step in the development of a system for identifying emotions in speech. Recently, the interaction between features generated from the same audio source was rarely considered, which may produce redundant features and increase the computational costs. To solve this problem, feature selection method based on correlation analysis and Fisher is proposed, which can remove the redundant features that have close correlations with each other. To improve the recognition performance of the feature subset after proposal feature selection further, an emotion recognition method based on extreme learning machine (ELM) decision tree is proposed according to the confusion degree among different basic emotions. A framework of speech emotion recognition is proposed and the classification experiments based on proposed classification method by using Chinese speech database from institute of automation of Chinese academy of sciences (CASIA) are performed. And the experimental results show that the proposal achieved 89.6% recognition rate on average. By proposal, it would be fast and efficient to discriminate emotional states of different speakers from speech, and it would make it possible to realize the interaction between speaker-independent and computer/robot in the future.

A facial expression emotion recognition based human-robot interaction system

A facial expression emotion recognition based human-robot interaction U+0028 FEER-HRI U+0029 system is proposed, for which a four-layer system framework is designed. The FEER-HRI system enables the robots not only to recognize human emotions, but also to generate facial expression for adapting to human emotions. A facial emotion recognition method based on 2D-Gabor, uniform local binary pattern U+0028 LBP U+0029 operator, and multiclass extreme learning machine U+0028 ELM U+0029 classifier is presented, which is applied to real-time facial expression recognition for robots. Facial expressions of robots are represented by simple cartoon symbols and displayed by a LED screen equipped in the robots, which can be easily understood by human. Four scenarios, i.e., guiding, entertainment, home service and scene simulation are performed in the human-robot interaction experiment, in which smooth communication is realized by facial expression recognition of humans and facial expression generation of robots within 2 seconds. As a few prospective applications, the FEER-HRI system can be applied in home service, smart home, safe driving, and so on.

Speech emotion recognition based on an improved brain emotion learning model

Abstract Human-robot emotional interaction has developed rapidly in recent years, in which speech emotion recognition plays a significant role. In this paper, a speech emotion recognition method based on an improved brain emotional learning (BEL) model is proposed, which is inspired by the emotional processing mechanism of the limbic system in the brain. The reinforcement learning rule of BEL model, however, makes it have poor adaptation and affects its performance. To solve these problems, Genetic Algorithm (GA) is employed to update the weights of BEL model. The proposal is tested on the CASIA Chinese emotion corpus, SAVEE emotion corpus, and FAU Aibo dataset, in which MFCC related features and their 1st order delta coefficients are extracted. In addition, the proposal is tested on INTERSPEECH 2009 standard feature set, in which three dimensionality reduction methods of Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), and PCA+LDA are used to reduce the dimension of feature set. The experimental results show that the proposed method obtains average recognition accuracy of 90.28% (CASIA), 76.40% (SAVEE), and 71.05% (FAU Aibo) for speaker-dependent (SD) speech emotion recognition and the highest average accuracy of 38.55% (CASIA), 44.18% (SAVEE), 64.60% (FAU Aibo) for speaker-independent (SI) speech emotion recognition are obtained, which shows that the proposal is feasible in speech emotion recognition.

Multimodal gesture recognition based on Choquet integral

A multimodal gesture recognition method is proposed based on Choquet integral by fusing information from camera and 3D accelerometer data. By calculating the optimal fuzzy measures for the camera recognition module and the accelerometer recognition module, the proposal obtains enough recognition rate 92.7% in average for 8 types of gestures by improving the recognition rate approximate 20% compared to that of each module. The proposed method aims to realize the casual communication from humans to robots by integrating nonverbal gesture messages and verbal messages.