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Research Article |

Speaker Identification Analysis Based on Long-Term Acoustic Characteristics with Minimal Performance

Author(s): Mahesh K. Singh1, S. Manusha2, K.V. Balaramakrishna3 and Sridevi Gamini4

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 10, Issue 4

Publisher : FOREX Publication

Published : 18 October 2022

e-ISSN : 2347-470X

Page(s) : 848-852

DOI: https://doi.org/10.37391/IJEER.100415

Abstract

The identity of the speakers depends on the phonological properties acquired from the speech. The Mel-Frequency Cepstral Coefficients (MFCC) are better researched for derived the acoustic characteristic. This speaker model is based on a small representation and the characteristics of the acoustic features. These are derived from the speaker model and the cartographic representation by the MFCCs. The MFCC is used for independent monitoring of speaker text. There is a problem with the recognition of speakers by small representation, so proposed the Gaussian Mixture Model (GMM), mean super vector core for training. Unknown vector modules are cleared using rarity and experiments based on the TMIT database. The I-vector algorithm is proposed for the effective improvement of ASR (Automatic Speaker Recognition). The Atom Aligned Sparse Representation (AASR) is used to describe the speaker-based model. The Short Representation Classification (SRC) is used to describe the speaker recognition report. A robust short coding is based on the Maximum Likelihood Estimation (MIE) to clarify the problem in small representation. Strong speaker verification based on a small representation of GMM super vectors. Strong speaker verification based on a small representation of GMM super vectors.

Keywords: GMM super vector, Robust sparse coding, MFCC, Speaker recognition, Sparse representation.




Mahesh K. Singh*, Department of ECE, Aditya Engineering College, Surampalem, India; Email: mahesh.singh@accendere.co.in

S. Manusha, Assistant Professor, Department of ECE, Aditya Engineering College, Surampalem, India; Email: sunkavallimanusha9977@gmail.com

K.V. Balaramakrishna, Department of ECE, Aditya Engineering College, Surampalem, India; Email: balaramakrishna_ece@acoe.edu.in

Sridevi Gamini, Assistant Professor, Department of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India; Email: sridevi_gamini@yahoo.com

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Mahesh K. Singh, S. Manusha, K.V. Balaramakrishna and Sridevi Gamini (2022), Speaker Identification Analysis Based on Long-Term Acoustic Characteristics with Minimal Performance . IJEER 10(4), 848-852. DOI: 10.37391/IJEER.100415.