Non-dominated Sorting Evolution Strategy-based K-means clustering algorithm for accent classification
Sameeh Ullah, Fakhri Karray, Jin-Myung Won · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · 2008
In this paper, a new method is proposed based on the side information and non-dominated sorting evolution strategy (NSES)-based K-means clustering algorithm. In a distance metric learning approach, data points are transformed to a new space where the Euclidean distances between similar and dissimilar points are at their minimum and maximum, respectively. However, the NSES-based K-means clustering yields globally optimized Gaussian components for an accent classification system. This hybrid clustering and classification approach enhances the performance of natural language call-routing systems. Accent classification performs the task of acoustic model switching based on the confidence measure for the callerpsilas query.