Auditory Evoked Potentials (AEPs) have recently gained attention as a biometric feature that may improve security and address reliability shortfalls of other commonly-used biometric features. The objective of this thesis is to investigate the accuracy with which subjects can be automatically identified or authenticated with machine learning (ML) techniques using a type of AEP known as the speech-evoked frequency following response (FFR). Accordingly, the results show more accurate discrimination between FFRs from different subjects than what has been reported in past studies. The accuracy improvement is searched either by optimized hyperparameter tuning of the ML model or extracting new features from FFRs and feeding them as inputs to the model. Finally, the accuracy of authenticating subjects using FFRs is investigated using a "sheep vs. wolves" scenario. The results of this work shed more light on the potential of use of speech-evoked FFRs in biometric identification and authentication systems.