Frame-Based Purification Implementation

When running the Speaker Diarization algorithm, each cluster is modeled with a variable number of Gaussian mixtures according to the amount of data it contains. It is necessary to analyze at what cluster complexity this behavior is present and the presented metrics can be used. In figure 4.12, the histograms of speech and non-speech (according to the reference file) are shown of metric 1 evaluated using models ranging from 1 to 8 mixtures. All model complexities have been trained with the same data and used to evaluate metric 1 on all the meeting in the same way as in figure 4.9.

Figure 4.12: Speech/non-speech histograms for different possible model complexities

It is seen that only the case of 1 Gaussian mixture shows a bigger overlap between the speech and non-speech histograms, while after 3 mixtures all plots seem identical (in fact, running the same experiments from 1 to 20 mixtures/model gives identical results from 9 to 20). The frame-level purification algorithm is therefore applied whenever the number of Gaussian mixtures is greater than one.

The algorithm is used when gathering the data to compare two clusters using the $\Delta$BIC metric in the following way:

1. Retrieve all frames assigned to each of two clusters and use either metric for each frame in both clusters.

2. If $M_{i}>1$, eliminate the $P$% of frames in each cluster with the highest computed metric, where $P$ is a value to be optimized according to the data.

3. Train two new models with the remaining data and use them for computing the $\Delta$BIC metric.