This paper presents an approach to emotion recognition from speech signals and textual content. In the analysis of speech signals, thirty-seven acoustic features are extracted from the speech input. Two different classifiers Support Vector Machines (SVMs) and BP neural network are adopted to classify the emotional states. In text analysis, we use the two-step classification method to recognize ...

Speech emotion is divided into four categories, Fear, Happy, Neutral and Surprise in this paper. Traditional features and their statistics are generally applied to recognize speech emotion. In order to quantify each feature’s contribution to emotion recognition, a method based on the Back Propagation (BP) neural network is adopted. Then we can obtain the optimal subset of the features. What’s m...

A crucial element of human–machine interaction, the automatic detection emotional states from human speech has long been regarded as a challenging task for machine learning models. One vital challenge in emotion recognition (SER) is how to learn robust and discriminative representations speech. Meanwhile, although methods have widely applied SER research, inadequate amount available annotated d...

During the last decade, Speech Emotion Recognition (SER) has emerged as an integral component within Human-computer Interaction (HCI) and other high-end speech processing systems. Generally, SER system targets speaker's existence of varied emotions by extracting classifying prominent features from a preprocessed signal. However, way humans machines recognize correlate emotional aspects signals ...

Speech Emotion Recognition (SER) system, which analyzes human utterances to determine a speaker’s emotion, has growing impact on how people and machines interact. Recent growth in human-computer interaction computational intelligence drawn the attention of many researchers Artificial Intelligence (AI) deep learning because its wider applicability several fields, including computer vision, natur...