A machine learning multi-dimensional acoustic feature vector authentication system comprising: at least one processor to execute machine readable instructions stored on at least one non-transitory computer readable medium; at least one data storage to store a plurality of multi-dimensional acoustic feature vector machine learning classifiers, wherein the plurality of multi-dimensional acoustic feature vector machine learning classifiers comprise convolutional neural networks (121A, 121B, 121C, 121D) trained to identify multi-dimensional acoustic feature vectors; wherein the at least one processor is to execute machine readable instructions to: extract at least one acoustic feature from a voice sample of a user; convert the acoustic feature into a plurality of multi-dimensional acoustic feature vectors (151A, 151B, 151C, 151D) using various acoustic feature extraction algorithms; apply each multi-dimensional acoustic feature vector in the plurality of multi-dimensional acoustic feature vectors to a corresponding multi-dimensional acoustic feature vector machine learning classifier from the plurality of multi-dimensional acoustic feature vector machine learning classifiers; determine a probability of spoofing for each multi-dimensional acoustic feature vector from an output of the corresponding multi-dimensional acoustic feature vector machine learning classifier; determine an overall probability of spoofing for the voice sample, based on the probability of spoofing for each multi-dimensional acoustic feature vector; determine whether the overall probability of spoofing is within a certain threshold; and in response to a determination that the overall probability of spoofing is within the certain threshold, authenticate the user. at least one processor to execute machine readable instructions stored on at least one non-transitory computer readable medium; at least one data storage to store a plurality of multi-dimensional acoustic feature vector machine learning classifiers, wherein the plurality of multi-dimensional acoustic feature vector machine learning classifiers comprise convolutional neural networks (121A, 121B, 121C, 121D) trained to identify multi-dimensional acoustic feature vectors; wherein the at least one processor is to execute machine readable instructions to: extract at least one acoustic feature from a voice sample of a user; convert the acoustic feature into a plurality of multi-dimensional acoustic feature vectors (151A, 151B, 151C, 151D) using various acoustic feature extraction algorithms; apply each multi-dimensional acoustic feature vector in the plurality of multi-dimensional acoustic feature vectors to a corresponding multi-dimensional acoustic feature vector machine learning classifier from the plurality of multi-dimensional acoustic feature vector machine learning classifiers; determine a probability of spoofing for each multi-dimensional acoustic feature vector from an output of the corresponding multi-dimensional acoustic feature vector machine learning classifier; determine an overall probability of spoofing for the voice sample, based on the probability of spoofing for each multi-dimensional acoustic feature vector; determine whether the overall probability of spoofing is within a certain threshold; and in response to a determination that the overall probability of spoofing is within the certain threshold, authenticate the user. extract at least one acoustic feature from a voice sample of a user; convert the acoustic feature into a plurality of multi-dimensional acoustic feature vectors (151A, 151B, 151C, 151D) using various acoustic feature extraction algorithms; apply each multi-dimensional acoustic feature vector in the plurality of multi-dimensional acoustic feature vectors to a corresponding multi-dimensional acoustic feature vector machine learning classifier from the plurality of multi-dimensional acoustic feature vector machine learning classifiers; determine a probability of spoofing for each multi-dimensional acoustic feature vector from an output of the corresponding multi-dimensional acoustic feature vector machine learning classifier; determine an overall probability of spoofing for the voice sample, based on the probability of spoofing for each multi-dimensional acoustic feature vector; determine whether the overall probability of spoofing is within a certain threshold; and in response to a determination that the overall probability of spoofing is within the certain threshold, authenticate the user.