Speaker identification analysis for SGMM with k-means and fuzzy C-means clustering using SVM statistical technique

Article type: Research Article

Authors: Manikandan, K.^{a; *} | Chandra, E.^b

Affiliations: [a] Department of Computer Science, PSG College of Arts and Science, Coimbatore, India | [b] Department of Computer Science, Bharathiar University, Coimbatore, India

Correspondence: [*] Corresponding author: K. Manikandan, Department of Computer Science, PSG College of Arts and Science, Coimbatore 641015, India. E-mail: [email protected].

Abstract: Speaker Identification denotes the speech samples of known speaker and it identifies the best matches of the input model. The SGMFC method is the combination of Sub Gaussian Mixture Model (SGMM) with the Mel-frequency Cepstral Coefficients (MFCC) for feature extraction. The SGMFC method minimizes the error rate, memory footprint and also computational throughput measure needs of a medium-vocabulary speaker identification system, supposed for preparation on a transportable or otherwise. Fuzzy C-means and k-means clustering are used in the SGMM method to attain the improved efficiency and their outcomes with parameters such as precision, sensitivity and specificity are compared.

Keywords: k-means, fuzzy C-means, SGMFC, speaker identification, SVM

DOI: 10.3233/KES-210073

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 25, no. 3, pp. 309-314, 2021