By continuing to browse this website, you implicitly agree with our Legal Disclaimer

This algorithm implements the Maximum-a-posteriori (MAP) estimation

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.

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

features | system/array_2d_floats/1 | Input |

id | system/text/1 | Input |

model | tutorial/gmm/1 | Output |

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

ubm | tutorial/gmm/1 | Input |

The code for this algorithm in Python

The ruler at 80 columns indicate suggested POSIX line breaks (for readability).

The editor will automatically enlarge to accomodate the entirety of your input

Use keyboard shortcuts for search/replace and faster editing. For example, use Ctrl-F (PC) or Cmd-F (Mac) to search through this box

For a given set of feature vectors and a Gaussian Mixture Models (GMM), this algorithm implements the Maximum-a-posteriori (MAP) estimation (adapting only the means).

Details of MAP estimation can be found in the paper: Reynolds, Douglas A., Thomas F. Quatieri, and Robert B. Dunn. "Speaker verification using adapted Gaussian mixture models." Digital signal processing 10.1 (2000): 19-41. A very good description on how the MAP estimation works can be found in the Mathematical Monks's YouTube channel.z

This algorithm relies on the Bob library.

No experiments are using this algorithm.

This algorithm was never executed.

Terms of Service | Contact Information | BEAT platform version 1.5.0b0 | © Idiap Research Institute - 2013-2019