Design and Validation of HindiSER:Speech Emotion Recognition Dataset for Hindi Language

Every human prefers to express their emotions in their mother tongue. In India, Hindi is considered the National Language and is spoken by around 45% of the population. The unavailability of emotional speech databases in many of the regional languages is a significant bottleneck in the domain of Human-Computer Interaction (HCI). To address this issue of Low-resource regional language, we have proposed a new Hindi language emotional speech database "HindiSER". This database has a total 520 emotional utterances recorded from 26 non-professional native Hindi speakers in the age group of 19-45 years. HindiSER has emotional utterances of happy, sad, neutral, and angry emotion classes of 5 emotionally neutral Hindi sentences. Thus, the number of utterances in each emotional class is 130. Objective validation is performed with Support Vector Machine (SVM) classifier, K-Nearest Neighbor (KNN) and also with Multi-Layer Perceptron (MLP) classifier. Subjective validation of HindiSER is done through a listening test. Performance comparison of these classifiers using spectral features shows better recall on the happy emotion class and lowest recall on neutral emotion.