An Improved Speech Emotion Classification Approach Based on Optimal Voiced Unit

Authors

Reda Elbarougy, Department of Information technology, Faculty of Computer and Information Sciences, Damietta University, New Damietta, EgyptFollow
Noha M El-Badry, Department of Mathematics, Faculty of Science, Damietta University, New Damietta, EgyptFollow
Mona Nagy ElBedwehy, Department of Computer Science, Faculty of Computer and Information Sciences, Damietta University, New Damietta, EgyptFollow

Abstract

Emotional speech recognition (ESR) has significant role in human-computer interaction. ESR methodology involves audio segmentation for selecting units to analyze, extract features relevant to emotion, and finally perform a classification process. Previous research assumed that a single utterance was the unit of analysis. They believed that the emotional state remained constant during the utterance, even though the emotional state could change over time, even within a single utterance. As a result, using an utterance as a single unit is ineffective for this purpose. The study’s goal is to discover a new voiced unit that can be utilized to improve ESR accuracy. Several voiced units based on voiced segments were investigated. To determine the best-voiced unit, each unit is evaluated using an ESR based on a support vector machine classifier. The proposed method was validated using three datasets: EMO-DB, EMOVO, and SAVEE. Experimental results revealed that a voiced unit with five-voiced segments has the highest recognition rate. The emotional state of the overall utterance is decided by a majority vote of its parts’ emotional states. The proposed method outperforms the traditional method in terms of classification outcomes. EMO-DB, EMOVO, and SAVEE improve their recognition rates by 12%, 27%, and 23%, respectively.

Recommended Citation

Elbarougy, Reda; M El-Badry, Noha; and Nagy ElBedwehy, Mona (2022) "An Improved Speech Emotion Classification Approach Based on Optimal Voiced Unit," Information Sciences Letters: Vol. 11 : Iss. 4 , PP -.
Available at: https://digitalcommons.aaru.edu.jo/isl/vol11/iss4/1