1. A processor-implemented method of providing voice authentication of a candidate speaker, comprising: accessing training data sets, wherein each training data set comprises data associated with a training speech sample of a speaker and a plurality of speaker metrics, wherein the plurality of speaker metrics include a native language of the speaker; using the training data sets to train a neural network, wherein the data associated with each training speech sample is a first training input to the neural network, and each of the plurality of speaker metrics is a second training input to the neural network, wherein the neural network is trained by executing a first task associated with a phonetic content senones classification and a second task associated with a native language classification, and wherein a relationship between the first task and the second task is parametric; receiving a speech sample of a candidate speaker; providing data associated with the speech sample to the neural network to generate a vector that contains values for the plurality of speaker metrics that includes a native language value for the candidate speaker based on the speech sample; and comparing the values contained in the vector to values contained in a reference vector associated with a known person to determine whether the candidate speaker is the known person.