Metadata-Version: 1.1
Name: xspear.fast_plda
Version: 1.1.1
Summary: Toolchains for speaker recognition and anti-spoofing using PLDA
Home-page: http://pypi.python.org/pypi/spear.fast_plda
Author: Aleksandr Sizov, Elie Khoury
Author-email: sizov@cs.uef.fi, Elie.Khoury@idiap.ch
License: GPLv3
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/
        
Keywords: bob,xbob,xbob.db,speaker recognition,plda
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
