algorithms tutorial gmm_scoring 4

Compute the GMM Scores

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.

Group: probes

Endpoint Name Data Format Nature
comparison_ids system/array_1d_uint64/1 Input
probe_statistics tutorial/gmm_statistics/1 Input
probe_id system/uint64/1 Input
probe_client_id system/uint64/1 Input
scores tutorial/probe_scores/1 Output

Group: templates

Endpoint Name Data Format Nature
template_client_id system/uint64/1 Input
template_id system/uint64/1 Input
template_model tutorial/gmm/1 Input

Unnamed group

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, a Gaussian Mixture Model (GMM) of the target client and an UBM-GMM, this algorithm computes the scoring using the linear scoring implemented on the Bob <https://www.idiap.ch/software/bob/docs/releases/last/sphinx/html/machine/generated/bob.machine.linear_scoring.html?highlight=linear%20scoring#bob.machine.linear_scoring>

This algorithm relies on the Bob library.

The inputs are:

• probe_statistics: A set of GMM Statistics of a probe.
• probe_id: A set of probe (class/subject) identifier as an unsigned 64 bits integer.
• ubm: A GMM corresponding to the Universal Background Model.
• template_id: Client (class/subject) identifier as an unsigned 64 bits integer.
• template_model: The GMM of the client

The output are the scores.

No experiments are using this algorithm.
This algorithm was never executed.