Access the number of speakers through visual access tendency for effective speech clustering

Speech clustering group the unlabeled speech utterances according to their similarity features and it requires prior information about number of speakers before assigning every speech utterance into its respective speaker cluster. Determine the number of speakers of speech dataset is a primary problem of speech clustering. Most methods follow the post clustering ideas for evaluation of number of speakers. After the recent study of cluster (or speakers) detection methods, it is found that visual access tendency (VAT) is most suitable approach for assessing the number of speakers information. However, it needs speaker model parameters for finding an accurate speakers information. By this motivation, the VAT is extended with Gaussian mixture model (GMM) for deriving of speakers information with model parameters. In the proposed work, speech data (i.e. speaker utterances or segment) is modeled by GMM, which derives GMM mean supervectors. Dissimilarity features are derived for a set of GMM mean supervectors in VAT for effective speech clustering. The GMM mean supervectors are high-dimensional and this dimensionality problem is addressed by generating intermediate vectors (i-vectors). Efficiency of proposed methods is demonstrated in the experimental study by real time datasets.