Bionic Wavelet Transform Entropy in Speaker-Independent and Context-Independent Emotional State Detection from Speech Signal

International Journal of Smart Electrical Engineering

دوره 11، شماره 03، آذر 2022، صفحه 131-136

نوع مقاله: Research Paper

شناسه دیجیتال (DOI): 10.30495/ijsee.2022.1950259.1167

نویسندگان

Mina Kadkhodaei Elyaderani؛ Hamid Mahmoodian^*

Najafabad Branch, Islamic Azad University, Najafabad, Iran

چکیده

The most common way of communication between humans is the use of speech signals, which also includes the person's emotional states. Bionic wavelet transform entropy has been considered in this study for speaker-independent and context-independent emotion detection from speech. Bionic wavelet Transform decomposition, using wavelet type Morlet, is used after preprocessing and Shannon entropy in its nodes is calculated for feature selection. In addition, prosodic features such as the first four formants, jitter or pitch deviation amplitude, and shimmer or energy variation amplitude besides MFCC features are applied to complete the feature vector. Support Vector Machine (SVM) is used to classify multi-class samples of emotions. 46 different utterances of a single sentence from the Berlin emotional speech dataset are selected to be analyzed. The emotions that have been considered are sadness, happiness, fear, boredom, anger, and normal emotional state. Experimental results show that proposed features can improve emotional state detection accuracy in the multi-class situation.

کلیدواژه‌ها

Bionic wavelet transform entropy؛ Feature selection؛ Speech emotion recognition؛ Support vector machine

سایر فایل های مرتبط با مقاله

vol11-no3-p3.pdf