NITK-KLESC: Kannada Language Emotional Speech Corpus for Speaker Recognition

Abstract

This work introduces an emotional speech dataset for Speaker Recognition (SR) task. The proposed dataset is recorded in the Kannada language from the people of Karnataka state of India. The speech dataset is collected by simulating five different emotions, such as Fear, Sad, Anger, Happy, and Neutral. The dataset is named as National Institute of Technology Karnataka, India- Kannada Language Emotional Speech Corpus (NITK-KLESC). The proposed dataset will be useful for SR tasks in various emotions. The proposed emotional speech dataset will be useful for emotion recognition, analysis of emotional speech, speech recognition, gender identification, and age identification of the age group 20 to 50 years. The proposed work describes the development, processing, analysis, acquisition, and evaluation of the proposed emotional speech dataset (NITK-KLESC). The analysis of emotional speech was done by considering various basic speech parameters like Pitch, Tempo, Intensity, and Zero Crossing Rate (ZCR). The characteristics of the dataset are reported using MFCC feature extraction and considered the CNN model as a classifier, compared with the existing EmoDB dataset. The average accuracy of the Emotional Speech Speaker Recognition (ESSR) task was measured at 84.44% with the EmoDB dataset and 95.2% with the proposed NITK-KLESC dataset. © 2023 IEEE.