Earticle

초록

영어

In this study, we propose an iterative clustering algorithm that automatically clusters a set of voice signal data without a label into an optimal number of clusters and generates hmm model for each cluster. In the clustering process, the likelihood calculations of the clusters are performed using iterative hmm learning and testing while varying the number of clusters for given data, and the maximum likelihood estimation method is used to determine the optimal number of clusters. We tested the effectiveness of this clustering algorithm on a small-vocabulary digit clustering task by mapping the unsupervised decoded output of the optimal cluster to the ground-truth transcription, we found out that they were highly correlated.

목차

Abstract
 1. Introduction
 2. Iterative clustering algorithm
 3. Computation of optimal number of clusters
 4. Experiment
 5. Conclusion
 References

키워드

저자

• Duker Ernest Junior [ Department of Computer Engineering, Hanbat National University, Korea ]
• Yoon Joong Kim [ Department of Computer Engineering, Hanbat National University, Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380