Psychological Stress Detection in Speech Using Return-to-opening Phase Ratios in Glottis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F15%3APU115748" target="_blank" >RIV/00216305:26220/15:PU115748 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5755/j01.eee.21.5.13336" target="_blank" >http://dx.doi.org/10.5755/j01.eee.21.5.13336</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5755/j01.eee.21.5.13336" target="_blank" >10.5755/j01.eee.21.5.13336</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Psychological Stress Detection in Speech Using Return-to-opening Phase Ratios in Glottis

Popis výsledku v původním jazyce

This paper is focused on investigation of psychological stress in speech signal using shapes of normalised glottal pulses. The pulses were estimated by two algorithms: the Direct Inverse Filtering and Iterative and Adaptive Inverse Filtering. Normalised glottal pulses are divided into opening and return phase, and a feature vector characterizing each glottal pulse is calculated for a series of n percentage interval in time domain. Each feature vector is created by parameters describing its return to opening phase ratio, namely chosen intervals, kurtosis, skewness, and area. Further, psychological stress is detected by feature vector and four different classifiers. Experimental results show, that the best accuracy approaching 95 % is reached with Gaussian Mixture Models classifier. All the best results were obtained regarding only the interval of 5 % from both phase durations, i.e. for and after pulse peak, where the most significant differences between normal and stressed speech in feature vector are occurred. Presented experiments were performed on our own speech database containing both real stressed speech and normal speech.

Název v anglickém jazyce

Psychological Stress Detection in Speech Using Return-to-opening Phase Ratios in Glottis

Popis výsledku anglicky

This paper is focused on investigation of psychological stress in speech signal using shapes of normalised glottal pulses. The pulses were estimated by two algorithms: the Direct Inverse Filtering and Iterative and Adaptive Inverse Filtering. Normalised glottal pulses are divided into opening and return phase, and a feature vector characterizing each glottal pulse is calculated for a series of n percentage interval in time domain. Each feature vector is created by parameters describing its return to opening phase ratio, namely chosen intervals, kurtosis, skewness, and area. Further, psychological stress is detected by feature vector and four different classifiers. Experimental results show, that the best accuracy approaching 95 % is reached with Gaussian Mixture Models classifier. All the best results were obtained regarding only the interval of 5 % from both phase durations, i.e. for and after pulse peak, where the most significant differences between normal and stressed speech in feature vector are occurred. Presented experiments were performed on our own speech database containing both real stressed speech and normal speech.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/LO1401" target="_blank" >LO1401: Interdisciplinární výzkum bezdrátových technologií</a><br>

Návaznosti

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

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Elektronika Ir Elektrotechnika

ISSN

1392-1215

e-ISSN

—

Svazek periodika

21

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

LT - Litevská republika

Počet stran výsledku

5

Strana od-do

59-63

Kód UT WoS článku

000362967700012

EID výsledku v databázi Scopus

2-s2.0-84944062307