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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27240/17:10237058

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/TF01000091" target="_blank" >TF01000091: Bezpečnost mobilních zařízení a komunikace</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

ISBN

978-1-5106-0901-3

ISSN

0277-786X

e-ISSN

neuvedeno

Počet stran výsledku

8

Strana od-do

"neuvedeno"

Název nakladatele

SPIE

Místo vydání

Bellingham

Místo konání akce

Anaheim

Datum konání akce

10. 4. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—