  Gender recognition is an essential component of automatic speech recognition
and interactive voice response systems. Determining gender of the speaker
reduces the computational burden of such systems for any further processing.
Typical methods for gender recognition from speech largely depend on features
extraction and classification processes. The purpose of this study is to
evaluate the performance of various state-of-the-art classification methods
along with tuning their parameters for helping selection of the optimal
classification methods for gender recognition tasks. Five classification
schemes including k-nearest neighbor, na\"ive Bayes, multilayer perceptron,
random forest, and support vector machine are comprehensively evaluated for
determination of gender from telephonic speech using the Mel-frequency cepstral
coefficients. Different experiments were performed to determine the effects of
training data sizes, length of the speech streams, and parameter tuning on
classification performance. Results suggest that SVM is the best classifier
among all the five schemes for gender recognition.
