Speaker Identification for the Improvement of the Security Communication between Law Enforcement Units

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F17%3A10237058" target="_blank" >RIV/61989100:27740/17:10237058 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27240/17:10237058

Result on the web

<a href="https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10200/1/Speaker-identification-for-the-improvement-of-the-security-communication-between/10.1117/12.2261796.short?SSO=1" target="_blank" >https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10200/1/Speaker-identification-for-the-improvement-of-the-security-communication-between/10.1117/12.2261796.short?SSO=1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/12.2261796" target="_blank" >10.1117/12.2261796</a>

Result language

angličtina

Original language name

Speaker Identification for the Improvement of the Security Communication between Law Enforcement Units

Original language description

This article discusses the speaker identification for the improvement of the security communication between law enforcement units. The main task of this research was to develop the text-independent speaker identification system which can be used for real-time recognition. This system is designed for identification in the open set. It means that the unknown speaker can be anyone. Communication itself is secured, but we have to check the authorization of the communication parties. We have to decide if the unknown speaker is the authorized for the given action. The calls are recorded by IP telephony server and then these recordings are evaluate using classification If the system evaluates that the speaker is not authorized, it sends a warning message to the administrator. This message can detect, for example a stolen phone or other unusual situation. The administrator then performs the appropriate actions. Our novel proposal system uses multilayer neural network for classification and it consists of three layers (input layer, hidden layer, and output layer). A number of neurons in input layer corresponds with the length of speech features. Output layer then represents classified speakers. Artificial Neural Network classifies speech signal frame by frame, but the final decision is done over the complete record. This rule substantially increases accuracy of the classification. Input data for the neural network are a thirteen Mel-frequency cepstral coefficients, which describe the behavior of the vocal tract. These parameters are the most used for speaker recognition. Parameters for training, testing and validation were extracted from recordings of authorized users. Recording conditions for training data correspond with the real traffic of the system (sampling frequency, bit rate). The main benefit of the research is the system developed for text-independent speaker identification which is applied to secure communication between law enforcement units.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20203 - Telecommunications

Project

<a href="/en/project/TF01000091" target="_blank" >TF01000091: Security of Mobile Devices and Communication</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of SPIE - The International Society for Optical Engineering. Volume 10200

ISBN

978-1-5106-0901-3

ISSN

0277-786X

e-ISSN

neuvedeno

Number of pages

8

Pages from-to

"neuvedeno"

Publisher name

SPIE

Place of publication

Bellingham

Event location

Anaheim

Event date

Apr 10, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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