Bag-of-words from image to speech: a multi-classifier emotions recognition system

Abstract

Recently, recognizing the emotional content of speech signals has received considerable research attention. Consequently, systems have been developed to recognize the emotional content of a spoken utterance. Achieving high accuracy in speech emotion recognition remains a challenging problem due to issues related to feature extraction, type, and size. Central to this study is increasing emotion recognition accuracy by porting the bag-of-word (BoW) technique from image to speech for feature processing and clustering. The BoW technique is applied to features extracted from Mel frequency cepstral coefficients (MFCC) which enhances feature quality. The study considers deployment of different classification approaches to examine the performance of the embedded BoW approach. The deployed classifiers include support vector machine (SVM), K-nearest neighbor (KNN), naive Bays (NB), random forest (RF), and extreme gradient boosting (XGBoost). In this study, experiments used the standard RAVDESS audio dataset with eight emotions: angry, calm, happy, surprised, sad, disgusted, fearful and neutral. The maximum accuracy obtained in the angry class using SVM was 85%, while overall accuracy was 80.1 %. The empirical works have proved that using BoW achieves better results in terms of accuracy and processing time compared to other available methods.