One Source to Detect them All: Gender, Age, and Emotion Detection from Voice
Syed Rohit Zaman, Dipan Sadekeen, M Aqib Alfaz, Rifat Shahriyar
Abstract
Gender, age, and emotion detection from the speech are essential in machine and human interaction. Sometimes it is required to categorize audios by age and gender from speech. Sometimes it is required to predict age, gender, and emotion from audio clips for investigation purposes. Most telecommunication companies need to analyze audio calls to predict customer demography and recommend offers based on demographic segments. Several researchers have focused on detecting gender, age, and emotion from different types of sources. But according to the best of our knowledge, none of them use a single type of source to detect all of them. We have introduced a system to detect gender, age, and emotion from audio speech. In our system, all audio files were converted into 20 statistical features, and the converted numerical datasets were used to create the different prediction models to attain the objective. The different prediction models are Random Forest, CatBoost, Gradient Boosting, K-nearest neighbors (KNN), XGBoost, AdaBoost, Decision Tree, Artificial neural networks (ANN), Naive Bayes, and Support vector machine (SVM). All the prediction models were evaluated and compared based on their test accuracy. In predicting gender, CatBoost performs best among all predictive models with 96.4% test accuracy. On the other hand, Random Forest performs best for predicting age among all predicting models with 70.4% test accuracy. For emotion prediction, XGBoost performs best with 66.1% test accuracy. It was also analyzed among 20 features which features are most influential for the effective prediction models. We believe that our findings will be beneficial to future researchers in this area.