Speaker segmentation and clustering

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 clustering, deterministic and probabilistic algorithms are examined. A comparative assessment of the reviewed algorithms is undertaken, the algorithm advantages and disadvantages are indicated, insight to the algorithms is offered, and deductions as well as recommendations are given. Rich transcription and movie analysis are candidate applications that benefit from combined speaker segmentation and clustering. © 2007 Elsevier B.V. All rights reserved

An exploration of methodologies to improve semi-supervised hierarchical clustering with knowledge-based constraints

Clustering algorithms with constraints (also known as semi-supervised clustering algorithms) have been introduced to the field of machine learning as a significant variant to the conventional unsupervised clustering learning algorithms. They have been demonstrated to achieve better performance due to integrating prior knowledge during the clustering process, that enables uncovering relevant useful information from the data being clustered. However, the research conducted within the context of developing semi-supervised hierarchical clustering techniques are still an open and active investigation area. Majority of current semi-supervised clustering algorithms are developed as partitional clustering (PC) methods and only few research efforts have been made on developing semi-supervised hierarchical clustering methods. The aim of this research is to enhance hierarchical clustering (HC) algorithms based on prior knowledge, by adopting novel methodologies. [Continues.

BH-centroids: A New Efficient Clustering Algorithm

The k-means algorithm is one of most widely used method for discovering clusters in data; however one of the main disadvantages to k-means is the fact that you must specify the number of clusters as an input to the algorithm. In this paper we present an improved algorithm for discovering clusters in data by first determining the number of clusters k, allocate the initial centroids, and then clustering data points by assign each data point to one centroid. We use the idea of Gravity, by assuming each data point in the cluster has a gravity that attract the other closest points, this leads each point to move toward the nearest higher gravity toward the nearest higher gravity point to have at the end one point for each cluster, which represent the centroid of that cluster. The measure of gravity of point (X) determined by its weight, which represent the number of points that use point X as the nearest point. Our algorithm employ a distance metric based (eg, Euclidean) similarity measure in order to determine the nearest or the similar point for each point. We conduct an experimental study with real-world as well as synthetic data sets to demonstrate the effectiveness of our techniques

Hierarchical Salient Object Detection for Assisted Grasping

Visual scene decomposition into semantic entities is one of the major challenges when creating a reliable object grasping system. Recently, we introduced a bottom-up hierarchical clustering approach which is able to segment objects and parts in a scene. In this paper, we introduce a transform from such a segmentation into a corresponding, hierarchical saliency function. In comprehensive experiments we demonstrate its ability to detect salient objects in a scene. Furthermore, this hierarchical saliency defines a most salient corresponding region (scale) for every point in an image. Based on this, an easy-to-use pick and place manipulation system was developed and tested exemplarily.Comment: Accepted for ICRA 201