This algorithm computes the score given a GMM and UBM using the Linear Scoring

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

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_text/1 Input
probe_statistics tutorial/gmm_statistics/1 Input
probe_id system/text/1 Input
probe_client_id system/text/1 Input
scores elie_khoury/string_probe_scores/1 Output

Group: templates

Endpoint Name Data Format Nature
template_client_id system/text/1 Input
template_id system/text/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.

Docutils System Messages

System Message: ERROR/3 (<string>, line 1); backlink

Unknown target name: "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".
No experiments are using this algorithm.
This algorithm was never executed.
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