Non-dominated Sorting Evolution Strategy-based K-means clustering algorithm for accent classification

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 dissimila...

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Bibliographic Details
Published in:2008 19th International Conference on Pattern Recognition pp. 1 - 4
Main Authors: Ullah, S., Karray, F., Jin-Myung Won
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2008
Subjects:
Acoustic measurements
Automatic speech recognition
Classification algorithms
Clustering algorithms
Computer aided instruction
Hidden Markov models
Humans
Performance evaluation
Routing
Sorting
ISBN:9781424421749, 1424421748
ISSN:1051-4651
Online Access:Get full text
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Summary: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.
ISBN:9781424421749
1424421748
ISSN:1051-4651
DOI:10.1109/ICPR.2008.4761644