An Improved Speech Emotion Classification Approach Based on Optimal Voiced Unit

Emotional speech recognition (ESR) has significant role in human-computer interaction. ESR methodology involves audio segmentation for selecting units to analyze, extract features relevant to emotion, and finally perform a classification process. Previous research assumed that a single utterance was the unit of analysis. They believed that the emotional state remained constant during the utterance, even though the emotional state could change over time, even within a single utterance. As a result, using an utterance as a single unit is ineffective for this purpose. The study’s goal is to discover a new voiced unit that can be utilized to improve ESR accuracy. Several voiced units based on voiced segments were investigated. To determine the best-voiced unit, each unit is evaluated using an ESR based on a support vector machine classifier. The proposed method was validated using three datasets: EMO-DB, EMOVO, and SAVEE. Experimental results revealed that a voiced unit with five-voiced segments has the highest recognition rate. The emotional state of the overall utterance is decided by a majority vote of its parts’ emotional states. The proposed method outperforms the traditional method in terms of classification outcomes. EMO-DB, EMOVO, and SAVEE improve their recognition rates by 12%, 27%, and 23%, respectively

COVID-19 Detection on Chest x-ray Images by Combining Histogram-oriented Gradient and Convolutional Neural Network Features

The COVID-19 coronavirus epidemic has spread rapidly worldwide after a person became infected with a severe health problem. The World Health Organization has declared the coronavirus a global threat (WHO). Early detection of COVID 19, particularly in cases with no apparent symptoms, may reduce the patients mortality rate. COVID 19 detection using machine learning techniques will aid healthcare systems around the world in recovering patients more rapidly. This disease is diagnosed using x-ray images of the chest; therefore, this study proposed a machine vision method for detecting COVID-19 in x-ray images of the chest. The histogram-oriented gradient (HOG) and convolutional neural network (CNN) features extracted from x-ray images were fused and classified using support vector machine (SVM) and softmax. The proposed feature fusion technique (99.36 percent) outperformed individual feature extraction methods such as HOG (87.34 percent) and CNN (93.64 percent)