On the effectiveness of MFCCs and their statistical distribution properties in speaker identification

This paper is devoted to the presence of distortions in a speech signal transmitted over a communication channel to a biometric system during voice-based remote identification. We propose to preliminary correct the frequency spectrum of the received signal based on the pre-distortion principle. Taking into account a priori uncertainty, a new information indicator of speech signal distortions and a method for measuring it in conditions of small samples of observations are proposed. An example of fast practical implementation of the method based on a parametric spectral analysis algorithm is considered. Experimental results of our approach are provided for three different versions of communication channel. It is shown that the usage of the proposed method makes it possible to transform the initially distorted speech signal into compliance on the registered voice template by using acceptable information discrimination criterion. It is demonstrated that our approach may be used in existing biometric systems and technologies of speaker identification.

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Unsupervised Clustering Method for Real Time Speaker Identification Systems

International Institute of Engineers (IIE) May 22-23, 2015 Dubai (UAE) ◽

10.15242/iie.e0515017 ◽

2015 ◽

Keyword(s):

Real Time ◽

Speaker Identification ◽

Unsupervised Clustering ◽

Clustering Method

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Bilingual speaker identification: Chinese and English

International Journal of Speech Language and the Law ◽

10.1558/ijsll.v22i1.18636 ◽

2015 ◽

Vol 22 (1) ◽

pp. 57-78 ◽

Cited By ~ 1

Author(s):

Peggy P.K. Mok ◽

Robert Bo Xu ◽

Donghui Zuo

Keyword(s):

Speaker Identification ◽

Bilingual Speaker ◽

Chinese And English

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Earwitness descriptions and speaker identification

International Journal of Speech Language and the Law ◽

10.1558/ijsll.v8i1.113 ◽

2007 ◽

Vol 8 (1) ◽

Author(s):

A. Daniel Yarmey

Keyword(s):

Speaker Identification

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New Feature Vectors using GFCC for Speaker Identification

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v6i8.146 ◽

2018 ◽

Vol 6 (8) ◽

pp. 243

Author(s):

A. Nagesh

Keyword(s):

Speaker Recognition ◽

Speaker Identification ◽

Signal To Noise Ratio ◽

Main Idea ◽

Extraction Methods ◽

Identification System ◽

Identification Performance ◽

Feature Vectors ◽

Overall Performance ◽

New Feature

The feature vectors of speaker identification system plays a crucial role in the overall performance of the system. There are many new feature vectors extraction methods based on MFCC, but ultimately we want to maximize the performance of SID system. The objective of this paper to derive Gammatone Frequency Cepstral Coefficients (GFCC) based a new set of feature vectors using Gaussian Mixer model (GMM) for speaker identification. The MFCC are the default feature vectors for speaker recognition, but they are not very robust at the presence of additive noise. The GFCC features in recent studies have shown very good robustness against noise and acoustic change. The main idea is GFCC features based on GMM feature extraction is to improve the overall speaker identification performance in low signal to noise ratio (SNR) conditions.

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