Clusters Merging and System Output

When computing the BIC metric between two clusters it was observed that small amounts of non-speech data affect negatively the speaker models and therefore could create errors when deciding wether to merge them or not. A new technique called frame-based cluster purification is introduced to modify the cluster models for the BIC comparison step in order to obtain more discriminant models. It is explained in detail in section 4.3.1.

It has also been observed that some clusters contain speaker segments from more than one speaker. The models associated with these clusters are able to model both speaker correctly and therefore cause a problem when comparing with other clusters containing either one of those speakers, leading to potentially serious decrease of performance due to erroneous cluster merges. For this reason, and during the initial iterations of the segmentation and clustering algorithm, a segment-level cluster purification algorithm aims at detecting speaker segments that are very dissimilar to the cluster they belong to, and assign them to a new cluster. A further description of the algorithm is given in section 4.3.2.

In order to save some computation when computing the $\Delta$BIC metric among all possible pairs, a pruning algorithm was implemented in broadcast news that would not compute the $\Delta$BIC for those pairs that had previously obtained a negative value. For the meetings system this was revisited and it was observed on development data that, specially during the initial iterations of the algorithm, the $\Delta$BIC metric would oscillate between small positive and negative values for some clusters until they would finally stabilize its assigned data. By using such a restrictive pruning, the system does not allow such clusters to eventually merge, even though they might be from the same speaker.

For this reason the pruning algorithm was relaxed to eliminate a cluster pair from further comparisons only if its $\Delta$BIC value falls below a certain threshold ($<0$), much safer to use. Such threshold was set to -100 as it was seen that $\Delta$BIC values below this threshold would always remain negative throughout the process and therefore there is no chance of eliminating any potential merge pair.

As in the broadcast news system, the cluster pair with the biggest $\Delta$BIC value is merged into a common cluster. The resulting model is the union of both merged models. If none of the cluster pairs obtains a positive $\Delta$BIC value, no merging takes place and the system prepares to finish, as it failed the stopping criterion.

In the Meetings system only the local $\Delta$BIC stopping criterion is used as tests using a likelihood criterion (in the same way as in the broadcast news system) resulted in worse performance.

When the system's stopping point is reached, the algorithm does a final post-processing in order to output a final clustering. During the iterative merging process the minimum speaker turn duration is set to be 3 seconds. This is necessary when there are many clusters, each containing small amounts of data, as the corresponding models can fluctuate a lot and not model the speaker appropriately.

Once the system determines to stop merging, the optimum amount of clusters has been reached and the models are expected to contain enough data to model each speaker appropriately. At this point, a single Viterbi segmentation iteration is performed where the minimum duration is set to 1.5 seconds in order to allow the output segmentation to contain smaller speaker turns, given that in meetings the average speaker turn duration is smaller than in broadcast news, as seen in section 3.2.