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June 10, 2026ICTACT Journal on Soft ComputingOpen Access

Fuzzy Clustering Algorithms - Comparative Studies for Noisy Speech Signals

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Authors

HVH Y VaniMAM A AnusuyaMCM L Chayadevi

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Overview

Comparative study showcases KFCM’s superior clustering of noisy speech signals, implying better recognition accuracy.

Key Points

  • The research aims to compare various clustering algorithms for their effectiveness in processing noisy speech signals.
  • Comparison of k-means, Fuzzy C Means, and Kernel Fuzzy C Means algorithms.
  • Assessment of clustering performance on homogeneous and heterogeneous speech datasets.
  • Evaluation of computation time and recognition accuracies for each clustering technique.
  • KFCM technique outperformed k-means and FCM in clustering noisy speech signals.
  • Higher recognition accuracy was reported for the KFCM algorithm compared to the others.
  • Clustering performances were systematically tabulated for analysis.

Cite This Study

Vani et al. (2019) studied this question.

synapsesocial.com/papers/6a28ff6a6f82f25be989c5ffhttps://doi.org/10.21917/ijsc.2019.0267
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