Audio–Visual Emotion-Aware Cloud Gaming Framework

Self Cite

A large number of the population around the world suffers from various disabilities. Disabilities affect not only children but also adults of different professions. Smart technology can assist the disabled population and lead to a comfortable life in an enhanced living environment (ELE). In this paper, we propose an effective voice pathology assessment system that works in a smart home framework. The proposed system takes input from various sensors, and processes the acquired voice signals and electroglottography (EGG) signals. Co-occurrence matrices in different directions and neighborhoods from the spectrograms of these signals were obtained. Several features such as energy, entropy, contrast, and homogeneity from these matrices were calculated and fed into a Gaussian mixture model-based classifier. Experiments were performed with a publicly available database, namely, the Saarbrucken voice database. The results demonstrate the feasibility of the proposed system in light of its high accuracy and speed. The proposed system can be extended to assess other disabilities in an ELE.

Section: Methodsmentioning

confidence: 99%

“…Using the Bayesian Sum Rule [23], we fuse the scores from the voice signal and the EGG signal, and find the final decision.…”

Section: Methodsmentioning

confidence: 99%

Enhanced Living by Assessing Voice Pathology Using a Co-Occurrence Matrix

Muhammad

Alhamid

Hossain

et al. 2017

Self Cite

“…As an expression of emotion, sound plays a very important role in human communication, and it has drawn wide attention from many institutions in the research fields of human–computer interaction (HCI) or human–robot interaction [1,2]. For instance, in the context of human–robot interaction, if the robot is able to recognize a person’s emotion through conversation, it could adopt appropriate behavior to interact well with that speaker.…”

Section: Introductionmentioning

confidence: 99%

Speech Emotion Recognition with Heterogeneous Feature Unification of Deep Neural Network

Jiang

Wang

Jin

et al. 2019

Automatic speech emotion recognition is a challenging task due to the gap between acoustic features and human emotions, which rely strongly on the discriminative acoustic features extracted for a given recognition task. We propose a novel deep neural architecture to extract the informative feature representations from the heterogeneous acoustic feature groups which may contain redundant and unrelated information leading to low emotion recognition performance in this work. After obtaining the informative features, a fusion network is trained to jointly learn the discriminative acoustic feature representation and a Support Vector Machine (SVM) is used as the final classifier for recognition task. Experimental results on the IEMOCAP dataset demonstrate that the proposed architecture improved the recognition performance, achieving accuracy of 64% compared to existing state-of-the-art approaches.

“…Use of emotion recognition is also getting prevalent in gaming where his emotional state governs a user’s interactions. Exergames (or gamercizing) frameworks [51,52], and other interactive and cloud-based gaming are becoming emotional-aware to provide a more immersive gaming experience [53,54]. The use of emotion recognition is also applicable in law for detecting and perceiving the intentions of the suspect [55] and in monitoring for risk prevention [56] and for smart health-care [57].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Emotion Intensities Using Machine Learning Algorithms: A Comparative Study

Mehta

Siddiqui

Javaid

2019

Over the past two decades, automatic facial emotion recognition has received enormous attention. This is due to the increase in the need for behavioral biometric systems and human–machine interaction where the facial emotion recognition and the intensity of emotion play vital roles. The existing works usually do not encode the intensity of the observed facial emotion and even less involve modeling the multi-class facial behavior data jointly. Our work involves recognizing the emotion along with the respective intensities of those emotions. The algorithms used in this comparative study are Gabor filters, a Histogram of Oriented Gradients (HOG), and Local Binary Pattern (LBP) for feature extraction. For classification, we have used Support Vector Machine (SVM), Random Forest (RF), and Nearest Neighbor Algorithm (kNN). This attains emotion recognition and intensity estimation of each recognized emotion. This is a comparative study of classifiers used for facial emotion recognition along with the intensity estimation of those emotions for databases. The results verified that the comparative study could be further used in real-time behavioral facial emotion and intensity of emotion recognition.