Gender Identification from Arabic Speech Using Machine Learning
Speech recognition is becoming increasingly used in real-world applications. One of the interesting applications is automatic gender recognition which aims to recognize male and female voices from short speech samples. This can be useful in applications such as automatic dialogue systems, system verification, prediction of demographic attributes (e.g., age, location) and estimating person’s emotional state. This paper focuses on gender identification from the publicly available dataset Arabic Natural Audio Dataset (ANAD) using an ensemble-classifier based approach. More specifically, initially we extended the original ANAD to include a gender label information through a manual annotation task. Next, in order to optimize the feature engineering process, a three stage machine learning approach is devised. In the first phase, re restricted to features to the two widely used ones; namely, MFCC and fundamental frequency coefficients. In the second phase, six distinct acoustic features were employed. Finally, in the third phase, the features were selected according to their associated weights in Random Forest Classifier, and the best features are thereby selected. The latter approach enabled us to achieve a classification rate of 96.02% on the test set generated with linear SVM classifier.