iv_scoring

iv_scoring.PLDA_scoring(enroll, test, ndx, mu, F, G, Sigma)

Compute the PLDA scores between to sets of vectors. The list of trials to perform is given in an Ndx object. PLDA matrices have to be pre-computed. i-vectors are supposed to be whitened before.

Parameters:

• enroll – a StatServer in which stat1 are i-vectors
• test – a StatServer in which stat1 are i-vectors
• ndx – an Ndx object defining the list of trials to perform
• mu – the mean vector of the PLDA gaussian
• F – the between-class co-variance matrix of the PLDA
• G – the within-class co-variance matrix of the PLDA
• Sigma – the residual covariance matrix

Returns:

a score object

iv_scoring.cosine_scoring(enroll, test, ndx, wccn=None)

Compute the cosine similarities between to sets of vectors. The list of trials to perform is given in an Ndx object.

Parameters:

• enroll – a StatServer in which stat1 are i-vectors
• test – a StatServer in which stat1 are i-vectors
• ndx – an Ndx object defining the list of trials to perform

Returns:

a score object

iv_scoring.mahalanobis_scoring(enroll, test, ndx, M)

Compute the mahalanobis distance between to sets of vectors. The list of trials to perform is given in an Ndx object.

Parameters:

• enroll – a StatServer in which stat1 are i-vectors
• ndx – an Ndx object defining the list of trials to perform
• M – mahalanobis matrix as a ndarray

Parm test:

a StatServer in which stat1 are i-vectors

Returns:

a score object

iv_scoring.two_covariance_scoring(enroll, test, ndx, W, B)

Compute the 2-covariance scores between to sets of vectors. The list of trials to perform is given in an Ndx object. Within and between class co-variance matrices have to be pre-computed.

Parameters:

• enroll – a StatServer in which stat1 are i-vectors
• test – a StatServer in which stat1 are i-vectors
• ndx – an Ndx object defining the list of trials to perform
• W – the within-class co-variance matrix to consider
• B – the between-class co-variance matrix to consider

Returns:

a score object