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Compute the GMM Statistics

This algorithm is a **legacy** one. The API has changed since its implementation. New versions and forks will need to be updated.

This algorithm is **splittable**

Algorithms have at least one
**input** and one **output**. All
algorithm endpoints are organized in **groups**.
Groups are used by the platform to indicate which inputs and
outputs are synchronized together. The first group is
automatically synchronized with the channel defined by the
block in which the algorithm is deployed.

Endpoint Name | Data Format | Nature |
---|---|---|

features | system/array_2d_floats/1 | Input |

statistics | tutorial/gmm_statistics/1 | Output |

Endpoint Name | Data Format | Nature |
---|---|---|

ubm | tutorial/gmm/1 | Input |

The code for this algorithm in Python

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For a given set of feature vectors and a Gaussian Mixture Model (GMM), this algorithm computes the 0_{th}, 1_{st} and 2_{nd} order GMM Statistics (Baum-Welch) relying on Bob implementation.

This algorithm relies on the Bob library.

The inputs are:

- features: A set of floating point vectors as a two-dimensional array (64 bits) of a client. The number of rows correspond to the number of samples, and the number of columns to the dimensionality of the samples.
- ubm: A GMM corresponding to the Universal Background Model.

The output are the statistics of the GMM of a given set of feature vectors (MAP adaptation).

This table shows the number of times this algorithm
has been **successfully** run using the given environment. Note
this does not provide sufficient information to evaluate if the
algorithm will run when submitted to different conditions.

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