Speech emotion classification using attention based network and regularized feature selection

Abstract

Speech emotion classification (SEC) has gained the utmost height and occupied a conspicuous position within the research community in recent times. Its vital role in Human–Computer Interaction (HCI) and affective computing cannot be overemphasized. Many primitive algorithmic solutions and deep neural network (DNN) models have been proposed for efficient recognition of emotion from speech however, the suitability of these methods to accurately classify emotion from speech with multi-lingual background and other factors that impede efficient classification of emotion is still demanding critical consideration. This study proposed an attention-based network with a pre-trained convolutional neural network and regularized neighbourhood component analysis (RNCA) feature selection techniques for improved classification of speech emotion. The attention model has proven to be successful in many sequence-based and time-series tasks. An extensive experiment was carried out using three major classifiers (SVM, MLP and Random Forest) on a publicly available TESS (Toronto English Speech Sentence) dataset. The result of our proposed model (Attention-based DCNN+RNCA+RF) achieved 97.8% classification accuracy and yielded a 3.27% improved performance, which outperforms state-of-the-art SEC approaches. Our model evaluation revealed the consistency of attention mechanism and feature selection with human behavioural patterns in classifying emotion from auditory speech.

Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition

“…Existing studies support these findings. For instance, Samson et al [ 53 ] demonstrated that selecting representative features significantly improves classification accuracy in speech emotion recognition. Similarly, Saisanthiya et al[ 54 ]and Raheel et al [ 55 ] showcased the performance enhancement achieved by integrating heterogeneous features such as text or physiological signals in sound recognition tasks.…”

Deep Learning, Ensemble and Supervised Machine Learning for Arabic Speech Emotion Recognition

“…KNN can perform regression and classification processes and K represents the number of nearest neighbors. KNN focuses on finding the smallest distance between neighbors, so it predicts the target based on the Euclidean distance (6). Figure 8 depicts the KNN classifier.…”

“…Computers use data to enhance their algorithm and better process other data in the future [2]. Researchers are looking for techniques to extract the emotional state of a speaker from their words and to successfully understand human emotions [6]. Emotions may be represented in speech and then exploited to extract useful connotations from spoken words, therefore improving speech recognition implementations [4,7].…”