Employing Emotion Cues to Verify Speakers in Emotional Talking Environments

Usually, people talk neutrally in environments where there are no abnormal talking conditions such as stress and emotion. Other emotional conditions that might affect people talking tone like happiness, anger, and sadness. Such emotions are directly affected by the patient health status. In neutral talking environments, speakers can be easily verified, however, in emotional talking environments, speakers cannot be easily verified as in neutral talking ones. Consequently, speaker verification systems do not perform well in emotional talking environments as they do in neutral talking environments. In this work, a two-stage approach has been employed and evaluated to improve speaker verification performance in emotional talking environments. This approach employs speaker emotion cues (text-independent and emotion-dependent speaker verification problem) based on both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) as classifiers. The approach is comprised of two cascaded stages that combines and integrates emotion recognizer and speaker recognizer into one recognizer. The architecture has been tested on two different and separate emotional speech databases: our collected database and Emotional Prosody Speech and Transcripts database. The results of this work show that the proposed approach gives promising results with a significant improvement over previous studies and other approaches such as emotion-independent speaker verification approach and emotion-dependent speaker verification approach based completely on HMMs.Comment: Journal of Intelligent Systems, Special Issue on Intelligent Healthcare Systems, De Gruyter, 201

International experience and approaches to the intellectual analysis of behavior in the e-government environment

The role of the Internet in people’s daily lives, the impact of social networks on the formation of public opinion, the spread of mobile communications, the collection of personal information in electronic information systems in the e-government environment made the problem of “behavior analysis” even more relevant. In order to improve the efficiency of the public administration process during the formation of the information society, one of the most important tasks to be performed by the government organizations is the correct assessment and prediction of citizens’ behavior and making the right decisions. The main goal of the intellectual analysis of behavior is to understand the logic of the activities of individuals and social groups. This article studies the international practice in intellectual analysis of behavior, examines the methods and algorithms used in this area, and identifies problems. Proposals are developed for the effective solution of questions on the intellectual analysis of behavior in the e-government environment. The approach we propose for intellectual analysis of behavior based on textual information consists of 4 levels: 1) primary processing, 2) document description, 3) classification of a set of documents into positive and negative classes, 4) determination of accuracy and completeness characteristics in classification. The use of semantic indicators for intellectual analysis of behavior can help conduct research with greater accuracy and effectively solve behavioral prediction problems