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Toolchains for fast and scalable PLDA

Project description

This package contains scripts that run the fast and scalable PLDA that was introduced in [1]. The package uses the framework of Bob Spear for handling the protocol, the toolchain and doing the post-processing (whitening and length-normalization).

If you use this package and/or its results, please you must cite the following publications:

[1] The original Fast PLDA paper published at S+SSPR 2014:

@inproceedings{Sizov,
  author = {Sizov, A and Lee, K.A. and Kinnunen, T.},
  title = {Unifying Probabilistic Linear Discriminant Analysis Variants in Biometric Authentication},
  booktitle = {Proc. S+SSPR},
  year = {2014},
  url = {to appear},
}

[2] The Spear paper published at ICASSP 2014:

@inproceedings{spear,
  author = {Khoury, E. and El Shafey, L. and Marcel, S.},
  title = {Spear: An open source toolbox for speaker recognition based on {B}ob},
  booktitle = {IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)},
  year = {2014},
  url = {http://publications.idiap.ch/downloads/papers/2014/Khoury_ICASSP_2014.pdf},
}

Installation

Just download this package and decompress it locally:

$ wget http://pypi.python.org/packages/source/x/xspear.fast_plda/xspear.fast_plda-1.0.0.zip
$ unzip xspear.fast_plda-1.0.0.zip
$ cd xspear.fast_plda-1.0.0.zip

Use buildout to bootstrap and have a working environment ready for experiments:

$ python bootstrap
$ ./bin/buildout

This also requires that bob (>= 1.2.0) is installed.

Example of use

The following command is intended to run the entire experiment for a protocol defined in “protocol.py”:

$  bin/ivec_whitening_lnorm.py -d protocol.py -t config/fast_plda.py -T PATH/TO/TEMP_DIR -U PATH/TO/RESULTS_DIR

For more details and options, please type:

$ bin/ivec_whitening_lnorm.py --help

Project details


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