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Determining the Optimal Number of Signal Clusters Using Iterative HMM Classification

  • Ernest, Duker Junior ;
  • Kim, Yoon Joong
  • Received : 2018.04.20
  • Accepted : 2018.05.09
  • Published : 2018.06.30

Abstract

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.

Keywords

HMM;Iterative Clustering;Optimal;Unsupervised;Signal

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