6. A computing device for determining user liveness comprising: a processor; and a memory configured to store data, said computing device being associated with a network and said memory being in communication with said processor and having instructions stored thereon which, when read and executed by said processor, cause said computing device to: capture voice biometric data of a user during a verification transaction, the user and computing device being a user-computing device pair; calculate a first matrix for the captured voice biometric data; calculate an expansion coefficient matrix based on the first matrix and a record spectral shape matrix, the record spectral shape matrix being for the user-computing device pair; calculate a distortion vector as the average of a ratio between the first matrix and the product of the record spectral shape matrix and the expansion coefficient matrix; calculate a spectral property difference as an element-wise function between the square value of the distortion vector and the square value of a user record normalization vector; input the spectral property difference into a machine learning algorithm; calculate an output score with the machine learning algorithm, the output score representing the difference between a probability the voice biometric data was captured from a live user and a probability the voice biometric data was not captured from a live user; and determine the voice biometric data was captured from a live user when the output score satisfies a threshold score.
6. A computing device for determining user liveness comprising: a processor; and a memory configured to store data, said computing device being associated with a network and said memory being in communication with said processor and having instructions stored thereon which, when read and executed by said processor, cause said computing device to: capture voice biometric data of a user during a verification transaction, the user and computing device being a user-computing device pair; calculate a first matrix for the captured voice biometric data; calculate an expansion coefficient matrix based on the first matrix and a record spectral shape matrix, the record spectral shape matrix being for the user-computing device pair; calculate a distortion vector as the average of a ratio between the first matrix and the product of the record spectral shape matrix and the expansion coefficient matrix; calculate a spectral property difference as an element-wise function between the square value of the distortion vector and the square value of a user record normalization vector; input the spectral property difference into a machine learning algorithm; calculate an output score with the machine learning algorithm, the output score representing the difference between a probability the voice biometric data was captured from a live user and a probability the voice biometric data was not captured from a live user; and determine the voice biometric data was captured from a live user when the output score satisfies a threshold score. 10. The computing device for determining user liveness in accordance with claim 6 , wherein the instructions that cause said computing device to calculate a first matrix when read and executed by said processor, further cause said computing device to: determine at least one portion of the captured voice biometric data; calculate a spectral vector for the at least one portion; and concatenate the spectral vectors to create the first matrix.