1. A system for audio authentication of an individual or speaker comprising: a sensor for one or more audio parameters associated with the individual; an extraction module for the acoustic parameters from an audio signal; a mean model or mean super-vector M representative of the population to be authenticated; a reference dictionary including a plurality of vectors and the mean super vector M of speech sequences representative of L reference speakers; a database containing public keys Kpub(l) of individuals to be authenticated; and a processor adapted for: generating a speaker dictionary representative of a speaker I to be authenticated by extracting vectors of speech sequences of the speaker I, and projecting the speaker I vectors onto the vectors of the reference dictionary; decomposing audio signals received by the sensor into vectors representative of the speaker I to be authenticated, transforming a super-vector V of the speaker I resulting from a concatenation of the vectors associated with the said speaker I into binary data taking as an input the mean super-vector M, and comparing the super-vector V of the speaker I with the mean super-vector M; and transmitting the binary data to a module for extracting the speaker authentication taking as an input the public keys Kpub(l) in order to at least one of authenticate the speaker and generate a cryptographic data item Ksec(l) associated with the speaker I; wherein the audio signal includes a speech signal, and wherein the processor is further adapted to: transform the speech signals into cepstral vectors; determine centroids associated with the cepstral vectors of a speaker and to execute transformation of the centroids into binary data using at least a significant bit, by executing the following steps: comparing the speaker super-vector V=(v i ) i=0,. .. L-1 with the mean super-vector M=(m i ) i=0,. .. L-1 ; and weighting of each centroid dependent on the number of vectors extracted from the associated speech signal; and transform coordinates of the speaker super-vector V into binary data in the following manner: the speaker is represented by the super vector V=(v i ) i=0,. .. L-1 , and the mean super-vector M=(m i ) i=0,. .. L-1 ; the processor executes the binarization b=(b j ) j=0,. .. 2L-l with one significant bit by the following coefficients: b 2 ⁢ i ⁢ b 2 ⁢ i + 1 = { 00 ⁢ ⁢ if ⁢ ⁢ v i ≤ m i 10 ⁢ ⁢ ⁢ if ⁢ ⁢ not in the case of a light centroid and in the case of a heavy centroid; σ=(σ i ) i=0,. .. nc-1 is the variance of the different coefficients calculated for all the centroids of all the speakers, wherein nc is the number of centroids of the reference dictionary; and n is a parameter permitting balancing of the binary sequence b; wherein the processor is adapted to execute the binarization b=(b i ) i=0,. .. 2L-1 with two significant bits by the following coefficients: b 2 ⁢ i ⁢ b 2 ⁢ i + 1 = { 00 ⁢ ⁢ if ⁢ ⁢ v i ≤ m i - n * σ i ⁢ ⁢ mod ⁡ ( nc - 1 ) 01 ⁢ ⁢ if ⁢ ⁢ m i - n * σ i ⁢ ⁢ mod ⁡ ( nc - 1 ) < v i ≤ m i 10 ⁢ ⁢ if ⁢ ⁢ m i < v i ≤ m i + n * σ i ⁢ ⁢ mod ⁡ ( nc - 1 ) 11 ⁢ ⁢ ⁢ if ⁢ ⁢ not .