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Self-organizing map classifier for stressed speech recognition

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86098030" target="_blank" >RIV/61989100:27240/16:86098030 - isvavai.cz</a>

  • Result on the web

    <a href="http://spie.org/Publications/Proceedings/Paper/10.1117/12.2224253" target="_blank" >http://spie.org/Publications/Proceedings/Paper/10.1117/12.2224253</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1117/12.2224253" target="_blank" >10.1117/12.2224253</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Self-organizing map classifier for stressed speech recognition

  • Original language description

    This paper presents a method for detecting speech under stress using Self-Organizing Maps. Most people who are exposed to stressful situations can not adequately respond to stimuli. Army, police, and fire department occupy the largest part of the environment that are typical of an increased number of stressful situations. The role of men in action is controlled by the control center. Control commands should be adapted to the psychological state of a man in action. It is known that the psychological changes of the human body are also reflected physiologically, which consequently means the stress effected speech. Therefore, it is clear that the speech stress recognizing system is required in the security forces. One of the possible classifiers, which are popular for its flexibility, is a self-organizing map. It is one type of the artificial neural networks. Flexibility means independence classifier on the character of the input data. This feature is suitable for speech processing. Human Stress can be seen as a kind of emotional state. Mel-frequency cepstral coefficients, LPC coefficients, and prosody features were selected for input data. These coefficients were selected for their sensitivity to emotional changes. The calculation of the parameters was performed on speech recordings, which can be divided into two classes, namely the stress state recordings and normal state recordings. The benefit of the experiment is a method using SOM classifier for stress speech detection. Results showed the advantage of this method, which is input data flexibility. (C) 2016 SPIE.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2016

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Proceedings of SPIE - The International Society for Optical Engineering

  • ISBN

    978-1-5106-0091-1

  • ISSN

    0277-786X

  • e-ISSN

  • Number of pages

    7

  • Pages from-to

  • Publisher name

    SPIE

  • Place of publication

    Baltimore

  • Event location

    Baltimore

  • Event date

    Apr 17, 2016

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article