MULTILINGUAL SPEAKER IDENTIFICATION SYSTEM THROUGH MULTIPLE FEATURES ANALYSIS OF SPEECH SIGNAL IN MULTILINGUAL ENVIRONMENT

2020 ◽  
Vol 8 (1) ◽  
pp. 27
Author(s):  
KUMAR JAIN VINAY ◽  
Keyword(s):  
Speech Signal ◽  
Speaker Identification ◽  
Identification System ◽  
Multiple Features

Method for measuring distortions of a speech signal during its transmission over a communication channel to a biometric identification system

2020 ◽  
pp. 65-72
Author(s):  
V. V. Savchenko ◽  
A. V. Savchenko
Keyword(s):  
Speech Signal ◽  
Communication Channel ◽  
Speaker Identification ◽  
A Priori ◽  
Small Samples ◽  
Practical Implementation ◽  
Identification System ◽  
New Information ◽  
Discrimination Criterion ◽  
A Priori Uncertainty

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.

scholarly journals Usage of Speech Signal Segmentation for the Construction of Complex Model in the Speaker Identification System

2014 ◽  
Vol 3 (26) ◽  
pp. 332
Author(s):  
Tatyana Vladimirovna Yermolenko ◽  
Nikita Sergeevich Klymenko
Keyword(s):  
Speech Signal ◽  
Speaker Identification ◽  
Complex Model ◽  
Identification System ◽  
Signal Segmentation

scholarly journals Optimised Features for Speaker Identification using Daubechies Wavelet based Variance Spectral Flux

2021 ◽  
Author(s):  
Chander Prabha ◽  
Sukhvinder Kaur ◽  
Meenu Gupta ◽  
Fadi Al-Turjman
Keyword(s):  
Speaker Recognition ◽  
Speech Signal ◽  
Speaker Identification ◽  
Speaker Verification ◽  
Information Criteria ◽  
Identification System ◽  
Discrete Wavelet ◽  
Specific Information ◽  
Spectral Flux ◽  
Approximation Coefficients

Abstract An important application of speech processing is speaker recognition, which automatically recognizes the person speaking in an audio recording, basis of which is speaker-specific information included in its speech features. It involves speaker verification and speaker identification. This paper presents an efficient method based on discrete wavelet transform and optimized variance spectral flux to enhance the enactment of speaker identification system. An effective feature extraction technique uses Daubechies 40 (db40) wavelet to compress and de-noised the speech signal by its decomposition into approximations and details coefficients at level 1. The approximation coefficients contain 99.9% of speech information as compared to detailed coefficients. So, the optimized variance spectral flux is applied on wavelet approximation coefficients which efficiently extract the frequency contents of the speech signal and gives unique features. The distance between extracted features has been obtained by applying traditional Bayesian information criteria. Experimental results were computed on recording data of 33 speakers (23 female and 10 males) for text independent identification of speaker. Evaluation of effectiveness of the proposed system is done by applying detection error trade-off curves, receiver operating characteristic, and area under curve. It shows 94.38% of speaker identification results when compared with traditional method using Mel frequency spectral coefficients which is 90.70%.

New Feature Vectors using GFCC for Speaker Identification

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.

A Word-Dependent Automatic Arabic Speaker Identification System

2008 ◽  
Author(s):  
Suliman S. Al-Dahri ◽  
Youssaf H. Al-Jassar ◽  
Yousef A. Alotaibi ◽  
Mansour M. Alsulaiman ◽  
Khondaker Abdullah-Al-Mamun
Keyword(s):  
Speaker Identification ◽  
Identification System ◽  
Arabic Speaker
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