A novel system for speaker diarization is proposed that combines the eigengap criterion and cluster ensembles. No explicit assumptions on the number of speakers are made. Two variants of the system are developed. The first variant does not cluster the speech segments that are detected as outliers, while the second one does. The aforementioned system variants are assessed with respect to various metrics, such as the overall classification error, the average cluster purity, and the average speaker purity. Experiments are conducted on two-person dialogue scenes in movies as well as on news broadcasts from MDE RT-03 Training Data Speech Corpus released by the U.S. National Institute of Standards and Technology. In the latter case, the diarization error rate is also reported. It is demonstrated that the clustering performance does not degrade when outliers are present. Moreover, thanks to the eigengap criterion, the evaluation metrics are improved.

Unsupervised speaker change detection is addressed in this paper. Three speaker segmentation systems are examined. The first system investigates the AudioSpectrumCentroid and the AudioWaveformEnvelope features, implements a dynamic fusion scheme, and applies the Bayesian Information Criterion (BIC). The second system consists of three modules. In the first module, a second-order statistic-measure is extracted; the Euclidean distance and the T2Hotelling statistic are applied sequentially in the second module; and BIC is utilized in the third module. The third system, first uses a metric-based approach, in order to detect potential speaker change points, and then the BIC criterion is applied to validate the previously detected change points. Experiments are carried out on a dataset, which is created by concatenating speakers from the TIMIT database. A systematic performance comparison among the three systems is carried out by means of one-way ANOVA method and post hoc Tukey's method.

This paper deals with N-dimensional patterns that are represented as points on the (N - 1)-dimensional simplex. The elements of such patterns could be the posterior class probabilities for N classes, given a feature vector derived by the Bayes classifier for example. Such patterns form N clusters on the (N - 1)-dimensional simplex. We are interested in reducing the number of clusters to N - 1 in order to redistribute the features assigned to a particular class in the N - 1 simplex over the remaining N - 1 classes in an optimal manner by using a self-organizing map. An application of the proposed solution to the re-assignment of emotional speech features classified as neutral into the emotional states of anger, happiness, surprise, and sadness on the Danish emotional speech database is presented