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.

Information Processing Systems in UAV Based on Bayesian Filtering in Conditions of Uncertainty

2020 ◽  
Vol 12 (4) ◽  
pp. 42-59
Author(s):  
Rinat Galiautdinov
Keyword(s):  
Information Processing ◽  
Degrees Of Freedom ◽  
Adaptive Filters ◽  
A Priori ◽  
Practical Implementation ◽  
Bayesian Filtering ◽  
Math Model ◽  
Digital Implementation ◽  
Estimation Algorithms ◽  
A Priori Uncertainty

In this article, the author considers the possibility of applying modern IT technologies to implement information processing algorithms in UAV motion control system. Filtration of coordinates and motion parameters of objects under a priori uncertainty is carried out using nonlinear adaptive filters: Kalman and Bayesian filters. The author considers numerical methods for digital implementation of nonlinear filters based on the convolution of functions, the possibilities of neural networks and fuzzy logic for solving the problems of tracking UAV objects (or missiles), the math model of dynamics, the features of the practical implementation of state estimation algorithms in the frame of added additional degrees of freedom. The considered algorithms are oriented on solving the problems in real time using parallel and cloud computing.

MULTI-EXPERT AND HYBRID CONNECTIONIST APPROACH FOR PATTERN RECOGNITION: SPEAKER IDENTIFICATION TASK

1994 ◽  
Vol 05 (03) ◽  
pp. 207-216 ◽  
Author(s):  
YOUNÈS BENNANI
Keyword(s):  
Markov Models ◽  
Speaker Identification ◽  
A Priori ◽  
Training Data ◽  
Identification System ◽  
Modular Systems ◽  
Connectionist System ◽  
Auto Regressive ◽  
Connectionist Approach ◽  
Better Than

This paper presents and evaluates a modular/hybrid connectionist system for speaker identification. Modularity has emerged as a powerful technique for reducing the complexity of connectionist systems, allowing a priori knowledge to be incorporated into their design. In problems where training data are scarce, such modular systems are likely to generalize significantly better than a monolithic connectionist system. In addition, modules are not restricted to be connectionist: hybrid systems, with e.g. Hidden Markov Models (HMMs), can be designed, combining the advantages of connectionist and non-connectionist approaches. Text independent speaker identification is an inherently complex task where the amount of training data is often limited. It thus provides an ideal domain to test the validity of the modular/hybrid connectionist approach. An architecture is developed in this paper which achieves this identification, based upon the cooperation of several connectionist modules, together with an HMM module. When tested on a population of 102 speakers extracted from the DARPA-TIMIT database, perfect identification was obtained. Overall, our recognition results are among the best for any text-independent speaker identification system handling this population size. In a specific comparison with a system based on multivariate auto-regressive models, the modular/hybrid connectionist approach was found to be significantly better in terms of both accuracy and speed. Our design also allows for easy incorporation of new speakers.

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%.

scholarly journals Increasing the accuracy of signal extraction by correcting the approximating function under conditions of a priori uncertainty

2021 ◽  
Vol 279 ◽  
pp. 02003
Author(s):  
Ivan Nikishin ◽  
Vladimir Marchuk ◽  
Igor Shrayfel ◽  
Ilya Sadrtdinov
Keyword(s):  
Additive Noise ◽  
A Priori ◽  
Signal Extraction ◽  
Statistical Characteristics ◽  
Practical Implementation ◽  
Sample Length ◽  
Complex Signals ◽  
Useful Signal ◽  
A Priori Uncertainty ◽  
Measured Process

The paper discusses the issues of practical implementation of increasing the accuracy of signal extraction, which is achieved by eliminating the «flip» of the approximating function when dividing the measured process into intervals under conditions of a priori uncertainty about the signal function, which significantly increases the error of allocating a useful signal. The probability of a «flip» of the approximating function depends significantly on the variance of the additive noise and the sample length. The use of the proposed methods and their software implementation makes it possible to increase the accuracy of the useful signal extraction up to 30 percent in the absence of a priori information about the function of the measured process for complex signals and at least 20% for simpler ones. The use of the proposed methods will significantly increase the processing efficiency in the conditions of a priori uncertainty about the function of the measured process (useful signal) and the statistical characteristics of the additive noise components.

Detection of Changes in the Random Signal Properties by Spectral Methods Under the Conditions of A Priori Uncertainty

1999 ◽  
Vol 53 (9-10) ◽  
pp. 1-10
Author(s):  
V. A. Omel'chenko ◽  
V. V. Balabanov ◽  
B. M. Bezruk ◽  
Yu. N. Goloborod'ko
Keyword(s):  
Spectral Methods ◽  
A Priori ◽  
Random Signal ◽  
A Priori Uncertainty

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.

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