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
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