sv_utils

This module provides miscellaneous tools that might be of use

to the different parts of a speaker recognition engine.

Copyright 2014-2015 Anthony Larcher

sv_utils provides utilities to facilitate the work with SIDEKIT.

sv_utils.check_file_list(inputFileList, fileDir, fileExtension)

Check the existence of a list of files in a specific directory Return a new list with the existing segments and a list of indices of those files in the original list. Return outputFileList and idx such that inputFileList[idx] = outputFileList

Parameters:

• inputFileList – list of file names
• fileDir – directory where to search for the files
• fileExtension – extension of the files to search for

Returns:

a list of existing files and the indices of the existing files in the input list

sv_utils.initialize_iv_extraction_eigen_decomposition(ubm, T)

Estimate matrices Q, D_bar_c and Tnorm, for approximation of the i-vectors. For more information, refers to [Glembeck09]

Parameters:

• ubm – Mixture object, Universal Background Model
• T – Raw TotalVariability matrix

Returns:

Q: Q matrix as described in [Glembeck11] D_bar_c: matrices as described in [Glembeck11] Tnorm: total variability matrix pre-normalized using the co-variance of the UBM

sv_utils.initialize_iv_extraction_fse(ubm, T)

Estimate matrices for approximation of the i-vectors. For more information, refers to [Cumani13]

Parameters:

• ubm – Mixture object, Universal Background Model
• T – Raw TotalVariability matrix

Returns:

Q: Q matrix as described in [Glembeck11] D_bar_c: matrices as described in [Glembeck11] Tnorm: total variability matrix pre-normalized using the co-variance of the UBM

sv_utils.initialize_iv_extraction_weight(ubm, T)

Estimate matrices W and T for approximation of the i-vectors For more information, refers to [Glembeck09]

Parameters:

• ubm – Mixture object, Universal Background Model
• T – Raw TotalVariability matrix as a ndarray

Returns:

W: fix matrix pre-computed using the weights from the UBM and the

total variability matrix

Tnorm: total variability matrix pre-normalized using the co-variance

of the UBM

sv_utils.read_svm(svmFileName)

Read SVM model in PICKLE format

Parameters:svmFileNale – name of the file to read from Returns:a tupple of weight and biais

sv_utils.save_svm(svmFileName, w, b)

Save SVM weights and biais in PICKLE format

Parameters:

• svmFileName – name of the file to write
• w – weight coefficients of the SVM to store
• b – biais of the SVM to store