Abstract: Emotion estimation method with Mel-frequency spectrum, voice power level and pitch frequency of human voices through CNN (Convolutional Neural Network) learning processes is proposed. Usually, frequency spectra are used for emotion estimation. The proposed method utilizes not only Mel-frequency spectrum, but also voice pressure level (voice power level) and pitch frequency to improve emotion estimation accuracy. These components are used through CNN learning processes using training samples which are provided by Keio University (emotional speech corpus) together with our own training samples which are collected by our students in emotion estimation processes. In these processes, the target emotion is divided into two categories, confident and non-confident. Through experiments, it is found that the proposed method is superior to the traditional method with only Mel-frequency by 15%.

Taiga Haruta, Mariko Oda and Kohei Arai, “Emotion Estimation Method with Mel-frequency Spectrum, Voice Power Level and Pitch Frequency of Human Voices through CNN Learning Processes” International Journal of Advanced Computer Science and Applications(IJACSA), 13(11), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131124

@article{Haruta2022,
title = {Emotion Estimation Method with Mel-frequency Spectrum, Voice Power Level and Pitch Frequency of Human Voices through CNN Learning Processes},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131124},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131124},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {11},
author = {Taiga Haruta and Mariko Oda and Kohei Arai}
}