Pilot Study of Emotion Recognition through Facial Expression

This paper presents our finding from a pilot study on human reaction through facial expression as well as brainwave changes when being induced by audio-visual stimuli while using the Emotiv Epoc equipment. We hypothesize that Emotiv Epoc capable to detect the emotion of the participants and the graphs would match with facial expression display. In this study, four healthy men were chosen and being induced with eight videos, six videos are predefined whereas the other two videos are personalized. We aim for identifying the optimum set up for the real experiment, to validate the capability of the Emotiv Epoc and to obtain spontaneous facial expression database. Thus, from the pilot study, the principal result shows that emotion is better if being induced by using personalized videos. Not only that, it also shows the brainwave produced by Emotiv Epoc is aligned with the facial expression especially for positive emotion cases. Hence, it is possible to obtain spontaneous database in the present of Emotiv Epoc

Speech emotion recognition based on SVM and KNN classifications fusion

Recognizing the sense of speech is one of the most active research topics in speech processing and in human-computer interaction programs. Despite a wide range of studies in this scope, there is still a long gap among the natural feelings of humans and the perception of the computer. In general, a sensory recognition system from speech can be divided into three main sections: attribute extraction, feature selection, and classification. In this paper, features of fundamental frequency (FEZ) (F0), energy (E), zero-crossing rate (ZCR), fourier parameter (FP), and various combinations of them are extracted from the data vector, Then, the principal component analysis (PCA) algorithm is used to reduce the number of features. To evaluate the system performance. The fusion of each emotional state will be performed later using support vector machine (SVM), K-nearest neighbor (KNN), In terms of comparison, similar experiments have been performed on the emotional speech of the German language, English language, and significant results were obtained by these comparisons

Investigating Prosodic Accommodation in Clinical Interviews with Depressed Patients

Six in-depth clinical interviews, involving six elderly female patients (aged 60+) and one female psychiatrist, were recorded and analysed for a number of prosodic accommodation variables. Our analysis focused on pitch, speaking time, and vowel-space ratio. Findings indicate that there is a dynamic manifestation of prosodic accommodation over the course of the interactions. There is clear adaptation on the part of the psychiatrist, even going so far as to have a reduced vowel-space ratio, mirroring a reduced vowel-space ratio in the depressed patients. Previous research has found a reduced vowel-space ratio to be associated with psychological distress; however, we suggest that it indicates a high level of adaptation on the part of the psychiatrist and needs to be considered when analysing psychiatric clinical interactions

In search of the role’s footprints in client-therapist dialogues

The goal of this research is to identify speaker's role via machine learning of broad acoustic parameters, in order to understand how an occupation, or a role, affects voice characteristics. The examined corpus consists of recordings taken under the same psychological paradigm (Process Work). Four interns were involved in four genuine client-therapist treatment sessions, where each individual had to train her therapeutic skills on her colleague that, in her turn, participated as a client. This uniform setting provided a unique opportunity to examine how role affects speaker's prosody. By a collection of machine learning algorithms, we tested automatic classification of the role across sessions. Results based on the acoustic properties show high classification rates, suggesting that there are discriminative acoustic features of speaker's role, as either a therapist or a client.info:eu-repo/semantics/publishedVersio

MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis

According to the World Health Organization, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labor-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation; the 3-electrode EEG signals of 55 subjects were recorded in resting state; the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis