Application of ABM to Spectral Features for Emotion Recognition

ER (Emotion Recognition) from speech signals has been among the attractive subjects lately. As known feature extraction and feature selection are most important process steps in ER from speech signals. The aim of present study is to select the most relevant spectral feature subset. The proposed method is based on feature selection with optimization algorithm among the features obtained from speech signals. Firstly, MFCC (Mel-Frequency Cepstrum Coefficients) were extracted from the EmoDB. Several statistical values as maximum, minimum, mean, standard deviation, skewness, kurtosis and median were obtained from MFCC. The next process of study was feature selection which was performed in two stages: In the first stage ABM (Agent-Based Modelling) that is hardly applied to this area was applied to actual features. In the second stageOpt-aiNET optimization algorithm was applied in order to choose the agent group giving the best classification success. The last process of the study is classification. ANN (Artificial Neural Network) and 10 cross-validations were used for classification and evaluation. A narrow comprehension with three emotions was performed in the application. As a result, it was seen that the classification accuracy was rising after applying proposed method. The method was shown promising performance with spectral features

Multistage Data Selection-based Unsupervised Speaker Adaptation for Personalized Speech Emotion Recognition

This paper proposes an efficient speech emotion recognition (SER) approach that utilizes personal voice data accumulated on personal devices. A representative weakness of conventional SER systems is the user-dependent performance induced by the speaker independent (SI) acoustic model framework. But, handheld communications devices such as smartphones provide a collection of individual voice data, thus providing suitable conditions for personalized SER that is more enhanced than the SI model framework. By taking advantage of personal devices, we propose an efficient personalized SER scheme employing maximum likelihood linear regression (MLLR), a representative speaker adaptation technique. To further advance the conventional MLLR technique for SER tasks, the proposed approach selects useful data that convey emotionally discriminative acoustic characteristics and uses only those data for adaptation. For reliable data selection, we conduct multistage selection using a log-likelihood distance-based measure and a universal background model. On SER experiments based on a Linguistic Data Consortium emotional speech corpus, our approach exhibited superior performance when compared to conventional adaptation techniques as well as the SI model framework

Successful Retention Strategies by Perfusion Managers to Reduce Perfusionist Attrition

Perfusion managers who lack strategies to mitigate perfusionist attrition place a strain on their remaining employees and incur replacement costs for their organization. Grounded in Vroom’s expectancy model, the purpose of this multiple case study was to explore strategies perfusion managers use to mitigate perfusionists attrition within open-heart centers in a northeast U.S. city. Data were collected through digital semistructured interviews with 5 licensed perfusionists who demonstrated successful staff retention. Perfusionist job listings were used as a secondary data source. Data were analyzed using Yin’s 5 step process. Four significant themes emerged: job satisfaction, work-life balance, surveys, and compensation. Perfusion managers may consider flexible scheduling to bolster job satisfaction and work-life balance. Perfusion managers should also survey employees to garner individual sentiments towards desired managerial strategies for job satisfaction, work-life balance, and compensation. The implications for positive social change from this study include the potential to enhance employee retention, resulting in improved quality of life for employees and safer, more consistent patient care