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Toolchains for speaker recognition and anti-spoofing using PLDA

Project description

Toolchain for fast and scalable PLDA
====================================

This package contains scripts that run the fast and scalable PLDA [1] and two-stage PLDA [2]. 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{Sizov2014,
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] Two-stage PLDA applied for anti-spoofing:

@article{Sizov2015,
title={Joint Speaker Verification and Anti-Spoofing in the i-Vector Space},
author={Sizov, A. and Khoury, E. and Kinnunen, T. and Wu, Z. and Marcel, S.},
journal={Information Forensics and Security, {IEEE} Transactions on},
volume={10},
number={4},
pages={821-832},
year={2015},
publisher={IEEE}
}

[3] The Spear paper published at ICASSP 2014::

@inproceedings{Khoury2014,
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-1.1.1.zip
$ unzip xspear.fast_plda-1.1.1.zip
$ cd xspear.fast_plda-1.1.1

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

$ python bootstrap.py
$ ./bin/buildout

This also requires that bob (== 1.2) is installed.


Example of use
--------------

To reproduce our spoofing experiments you need to download the data
$ wget http://www.idiap.ch/resource/biometric/data/TIFS2015.zip
$ unzip TIFS2015.zip

and modify necessary directories for the scripts/TIFS2015/reproduce_* shell scripts.

For more details and options, please use --help option for the executable files in the bin/ directory:

$ bin/ivec_whitening_lnorm.py --help

.. _Spear: https://pypi.python.org/pypi/bob.spear/

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1.1.1

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