Implements the Universal Background Model (UBM) training
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The code for this algorithm in Python
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For a Gaussian Mixture Models (GMM), this algorithm implements the Universal Background Model (UBM) training described in [Reynolds2000].
First, this algorithm estimates the means, diagonal covariance matrix and the weights of each gaussian component using the KMeans clustering. After, only the means are re-estimated using the Maximum Likelihood (ML) estimator.
This algorithm relies on the Bob library.
The input, features, is a training set of floating point vectors as a two-dimensional array of floats (64 bits), the number of rows corresponding to the number of training samples, and the number of columns to the dimensionality of the training samples. The output, ubm, is the GMM trained using the ML estimator.
|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.