Speech Emotion Recognition (SER) is a popular topic in academia and industry. Feature engineering plays a pivotal role in building an efficient SER. Although researchers have done a tremendous amount of work in this field, there are still the issues of speech feature choice and the correct application of feature engineering that remains to be solved in the domain of SER. In this research, a feature optimization approach that uses a clustering-based genetic algorithm is proposed. Instead of randomly selecting the new generation, clustering is applied at the fitness evaluation level to detect outliers for exclusion to be part of the next generation. The approach is compared with the standard Genetic Algorithm in the context of audio emotion recognition using Berlin Emotional Speech Database (EMO-DB), Ryerson Audio-Visual Database of Speech and Song (RAVDESS) and, Surrey Audio-Visual Expressed Emotion Dataset (SAVEE). Results signify that the proposed technique effectively improved the emotion classification in speech. The recognition rate of 89.6% for general speakers (both male and female), 86.2% for male speakers, and 88.3% for female speakers on EMO-DB, 82.5% for general speakers, 75.4% for male speakers, and 91.1% for female speaker on RAVDESS, and 77.7% for general speakers on SAVEE is obtained in speaker-dependent experiments. For speaker-independent experiments, we achieved the recognition rate of 77.5% on EMO-DB, 76.2% on RAVDESS and, 69.8 % on SAVEE. All the experiments were performed on MATLAB and the Support Vector Machine (SVM) was used for classification. Results confirm that the proposed method is capable of discriminating emotions effectively and performed better than the other approaches used for comparison in terms of performance measures.

The advancement in technology especially in the field of artificial intelligence has opened up novel and robust ways to reanalyze the many aspects of human emotional behavior. One of such behavioral studies is the cultural impact on the expression and perception of human emotions. In-group advantage makes it easy for the people of the same cultural group to perceive each other’s emotions accurately. The goal of this research is to re-investigate human behavior regarding expression and perception of emotions in speech. The theoretical basis of this research is grounded on the dialect theory of emotions. For the purpose of this study, six datasets of audio speeches have been considered. The participants of these datasets belong to six different cultural areas. A fully automated, machine learning-based framework i.e. Support Vector Machine (SVM) is used to carry out this study. The overall emotion perception for all six cultural groups supports in-group advantage, whereas emotion wise analysis partially supports the In-group advantage.

Robust speech emotion recognition relies on the quality of the speech features. We present speech features enhancement strategy that improves speech emotion recognition. We used the INTERSPEECH 2010 challenge feature-set. We identified subsets from the features set and applied principle component analysis to the subsets. Finally, the features are fused horizontally. The resulting feature set is analyzed using t-distributed neighbour embeddings (t-SNE) before the application of features for emotion recognition. The method is compared with the state-of-the-art methods used in the literature. The empirical evidence is drawn using two well-known datasets: Berlin Emotional Speech Dataset (EMO-DB) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) for two languages, German and English, respectively. Our method achieved an average recognition gain of 11.5% for six out of seven emotions for the EMO-DB dataset, and 13.8% for seven out of eight emotions for the RAVDESS dataset as compared to the baseline study.