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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2008 19th International Conference on Pattern Recognition s. 1 - 4
Hlavní autoři: Ullah, S., Karray, F., Jin-Myung Won
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.12.2008
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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