An Overview of SIDEKIT
======================

| **SIDEKIT** aims at providing the whole chain of tools required to perform speaker recognition.
| The main tools available include:

   * Acoustic features extraction

      - Linear-Frequency Cepstral Coefficients (LFCC)
      - Mel-Frequency Cepstral Coefficients (MFCC)
      - RASTA filtering
      - Energy-based Voice Activity Detection (VAD)
      - normalization (CMS, CMVN, Short Term Gaussianization)

   * Modeling and classification

      - Gaussian Mixture Models (GMM)
      - *i* - vectors
      - Probabilistic Linear Discriminant Analysis (PLDA)
      - Joint Factor Analysis (JFA)
      - Support Vector Machine (SVM)
      - Deep Neural Network (bridge to THEANO)

   * Presentation of the results
      - DET plot
      - ROC Convex Hull based DET plot


Implementation
--------------

| **SIDEKIT** has been designed and written in `Python <https://www.python.org>`_ and released under LGPL :ref:`license`
| to allow a wider usage of the code that, we hope, could be beneficial to the community.
| The structure of the core package makes use of a limited number of classes in order
| to facilitate the readability and reusability of the code.
| Starting from version 1.1.0 SIDEKIT is no longer tested under Python 2.*
| **SIDEKIT** has been tested under Python 3.7 for both Linux and MacOS.


About SIDEKIT
-------------

    :Authors:
            Anthony Larcher \& Kong Aik Lee \& Sylvain Meignier

    :Version: 1.3.1 of 2019/01/22

To know about the version and license of **SIDEKIT** ::

   sidekit.__version__
   sidekit.__license__

.. toctree::
   :maxdepth: 2

   license.rst
   compatibilities.rst