Performance Evaluation of Different Modeling Methods and Classifiers with MFCC and IHC Features for Speaker Recognition

Paulose, Suma; Mathew, Dominic; Abraham, Thomas

doi:10.1016/j.procs.2017.09.076

Cited by 22 publications

(12 citation statements)

References 7 publications

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“…Short term spectral features are extracted from speech signals by dividing them into small frames of lengths of 20-30ms. [4].…”

Section: Feature Extraction Techniquesmentioning

confidence: 99%

“…In contrast with MFCC, IHC looks after the physiological variation in the auditory system of mammals. Paulose et al [4] used this technique with MFCC, pitch, and formants to observe the performance of several speaker modeling techniques when used with MFCC and IHC. The results show that MFCC is more quality than IHC, especially when combined with pitch and formants.…”

Section: Inner Hair Cell Coefficients (Ihc)mentioning

confidence: 99%

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Speaker Recognition Systems in the Last Decade – A Survey

Ahmed¹,

Hassan²

2021

ETJ

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Speaker Recognition Defined by the process of recognizing a person by his\her voice through specific features that extract from his\her voice signal. An Automatic Speaker recognition (ASP) is a biometric authentication system. In the last decade, many advances in the speaker recognition field have been attained, along with many techniques in feature extraction and modeling phases. In this paper, we present an overview of the most recent works in ASP technology. The study makes an effort to discuss several modeling ASP techniques like Gaussian Mixture Model GMM, Vector Quantization (VQ), and Clustering Algorithms. Also, several feature extraction techniques like Linear Predictive Coding (LPC) and Mel frequency cepstral coefficients (MFCC) are examined. Finally, as a result of this study, we found MFCC and GMM methods could be considered as the most successful techniques in the field of speaker recognition so far.

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“…Short term spectral features are extracted from speech signals by dividing them into small frames of lengths of 20-30ms. [4].…”

Section: Feature Extraction Techniquesmentioning

confidence: 99%

Section: Inner Hair Cell Coefficients (Ihc)mentioning

confidence: 99%

Speaker Recognition Systems in the Last Decade – A Survey

Ahmed¹,

Hassan²

2021

ETJ

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“…The authors S. Paulose and A. Thomas [10] introduces an automated speaker endorsement technique which identifies the person from the feature coefficients included in the speech signal. The proposed system is applicable to several security application.…”

Section: Related Workmentioning

confidence: 99%

“…This model is a heavy sum of Gaussian distributions capable of determining a random separation of supervision. The equation of likelihood method of a GMM for an examination of x is given as below [10] In the above equation, n  and n  are the matrices of covariance and the mean vector of the n th Gaussian, sequentially.…”

Section: ) Gaussian Mixture Modelmentioning

confidence: 99%

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A Speaker Recognition System Using Gaussian Mixture Model, EM Algorithm and K-Means Clustering

Jadhav¹,

Dharwadkar²

2018

IJMECS

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The automated speaker endorsement technique used for recognition of a person by his voice data. The speaker identification is one of the biometric recognition and they were also used in government services, banking services, building security and intelligence services like this applications. The exactness of this system is based on the pre-processing techniques used to select features produced by the voice and to identify the speaker, the speech modeling methods, as well as classifiers, are used. Here, the edges and continuous quality point are eliminated in the normalization process. The Mel-Scale Frequency Cepstral Coefficient is one of the methods to grab features from a wave file of spoken sentences. The Gaussian Mixture Model technique is used and done experiments on MARF (Modular Audio Recognition Framework) framework to increase outcome estimation. We have presented an end pointing elimination in Gaussian selection medium for MFCC.

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