Comparison of clustering methods: A case study of text-independent speaker modeling

  • Authors:

  • Tomi Kinnunen;Ilja Sidoroff;Marko Tuononen;Pasi Fränti

  • Affiliations:

  • Speech and Image Processing Unit, School of Computing, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland;Speech and Image Processing Unit, School of Computing, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland;Speech and Image Processing Unit, School of Computing, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland;Speech and Image Processing Unit, School of Computing, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2011

Quantified Score

Hi-index 0.11

Visualization

Abstract

Clustering is needed in various applications such as biometric person authentication, speech coding and recognition, image compression and information retrieval. Hundreds of clustering methods have been proposed for the task in various fields but, surprisingly, there are few extensive studies actually comparing them. An important question is how much the choice of a clustering method matters for the final pattern recognition application. Our goal is to provide a thorough experimental comparison of clustering methods for text-independent speaker verification. We consider parametric Gaussian mixture model (GMM) and non-parametric vector quantization (VQ) model using the best known clustering algorithms including iterative (K-means, random swap, expectation-maximization), hierarchical (pairwise nearest neighbor, split, split-and-merge), evolutionary (genetic algorithm), neural (self-organizing map) and fuzzy (fuzzy C-means) approaches. We study recognition accuracy, processing time, clustering validity, and correlation of clustering quality and recognition accuracy. Experiments from these complementary observations indicate clustering is not a critical task in speaker recognition and the choice of the algorithm should be based on computational complexity and simplicity of the implementation. This is mainly because of three reasons: the data is not clustered, large models are used and only the best algorithms are considered. For low-order models, choice of the algorithm, however, can have a significant effect.