This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker clu...

-Probabilistic graphs have significant importance in data mining. The correlations endure amid the adjacent edges in different probabilistic graphs. Graph clustering is used in exploratory data analysis at data compression, information retrieval and image segmentation. The existing work presented a Partially Expected Edit Distance Reduction (PEEDR) and Correlated Probabilistic Graphs Spectral (...

Text clustering is an established technique for improving quality in information retrieval, for both centralized and distributed environments. However, for highly distributed environments, such as peer-topeer networks, current clustering algorithms fail to scale. Our algorithm for peer-to-peer clustering achieves high scalability by using a probabilistic approach for assigning documents to clus...

Traditional clustering algorithms are designed to search for a single clustering solution despite the fact that multiple alternative clustering solutions might exist for a particular dataset. For example, a set of news articles might be clustered by topic or by the author’s gender or age. Similarly, book reviews might be clustered by sentiment or comprehensiveness. In this paper, we address the...

In Quantum Mechanics the transition from a deterministic description to a probabilistic one is done using a simple rule termed the Born rule. This rule states that the probability of an outcome (a) given a state (Ψ) is the square of their inner products ((a>Ψ)2). In this paper, we unravel a new probabilistic justification for popular algebraic algorithms, based on the Born rule. These algorithm...

 In this paper, utilization of clustering algorithms for data fusion in decision level is proposed. The results of automatic isolated word recognition, which are derived from speech spectrograph and Linear Predictive Coding (LPC) analysis, are combined with each other by using fuzzy clustering algorithms, especially fuzzy k-means and fuzzy vector quantization. Experimental results show that the...