Robust and complex approach of pathological speech signal
analysis

J 2015

Robust and complex approach of pathological speech signal analysis

MEKYSKA, Jiří, Eva JANOUŠOVÁ, Pedro GOMEZ-VILDA, Zdeněk SMÉKAL, Irena REKTOROVÁ et. al.

Základní údaje

Originální název

Robust and complex approach of pathological speech signal analysis

Autoři

MEKYSKA, Jiří (203 Česká republika), Eva JANOUŠOVÁ (203 Česká republika, domácí), Pedro GOMEZ-VILDA (724 Španělsko), Zdeněk SMÉKAL (203 Česká republika), Irena REKTOROVÁ (203 Česká republika, garant, domácí), Ilona ELIÁŠOVÁ (203 Česká republika, domácí), Milena KOŠŤÁLOVÁ (203 Česká republika, domácí), Martina MRAČKOVÁ (203 Česká republika, domácí), Jesus B. ALONSO-HERNANDEZ (724 Španělsko), Marcos FAUNDEZ-ZANUY (724 Španělsko) a Karmele LOPEZ-DE-IPINA (724 Španělsko)

Vydání

Neurocomputing, AMSTERDAM, ELSEVIER SCIENCE BV, 2015, 0925-2312

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 2.392

Kód RIV

RIV/00216224:14740/15:00085054

Organizační jednotka

Středoevropský technologický institut

DOI

http://dx.doi.org/10.1016/j.neucom.2015.02.085

UT WoS

000358808500012

Klíčová slova anglicky

Pathological speech; Disordered voice; Dysarthria; Speech processing; Bicepstrum; Non-linear dynamic features

Štítky

podil, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 4. 4. 2016 14:19, Mgr. Eva Špillingová

Anotace

V originále

This paper presents a study of the approaches in the state-of-the-art in the field of pathological speech signal analysis with a special focus on parametrization techniques. It provides a description of 92 speech features where some of them are already widely used in this field of science and some of them have not been tried yet (they come from different areas of speech signal processing like speech recognition or coding). As an original contribution, this work introduces 36 completely new pathological voice measures based on modulation spectra, inferior colliculus coefficients, bicepstrum, sample and approximate entropy and empirical mode decomposition. The significance of these features was tested on 3 (English, Spanish and Czech) pathological voice databases with respect to classification accuracy, sensitivity and specificity. To our best knowledge the introduced approach based on complex feature extraction and robust testing outperformed all works that have been published already in this field. The results (accuracy, sensitivity and specificity equal to 100.0 +/- 0.0%) are discussable in the case of Massachusetts Eye and Ear Infirmary (MEEI) database because of its limitation related to a length of sustained vowels, however in the case of Principe de Asturias (PdA) Hospital in Alcala de Henares of Madrid database we made improvements in classification accuracy (82.1 +/- 3.3%) and specificity (83.8 +/- 5.1%) when considering a single-classifier approach. Hopefully, large improvements may be achieved in the case of Czech Parkinsonian Speech Database (PARCZ), which are discussed in this work as well. All the features introduced in this work were identified by Mann-Whitney U test as significant (p < 0.05) when processing at least one of the mentioned databases. The largest discriminative power from these proposed features has a cepstral peak prominence extracted from the first intrinsic mode function (p = 6.9443 x 10(-32)) which means, that among all newly designed features those that quantify especially hoarseness or breathiness are good candidates for pathological speech identification. The paper also mentions some ideas for the future work in the field of pathological speech signal analysis that can be valuable especially under the clinical point of view. (C) 2015 Elsevier B.V. All rights reserved.

Návaznosti

ED1.1.00/02.0068, projekt VaV

Název: CEITEC - central european institute of technology

NT13499, projekt VaV

Název: Řeč, její poruchy a kognitivní funkce u Parkinsonovy nemoci

Přiložené soubory

ZVV_2015_140_1319700_Robust_and_complex.pdf

Požádat o autorskou verzi souboru

Citovat

MEKYSKA, Jiří, Eva JANOUŠOVÁ, Pedro GOMEZ-VILDA, Zdeněk SMÉKAL, Irena REKTOROVÁ, Ilona ELIÁŠOVÁ, Milena KOŠŤÁLOVÁ, Martina MRAČKOVÁ, Jesus B. ALONSO-HERNANDEZ, Marcos FAUNDEZ-ZANUY a Karmele LOPEZ-DE-IPINA. Robust and complex approach of pathological speech signal analysis. Neurocomputing. AMSTERDAM: ELSEVIER SCIENCE BV, 2015, roč. 167, November, s. 94-111. ISSN 0925-2312. Dostupné z: https://dx.doi.org/10.1016/j.neucom.2015.02.085.

@article{1319700,
   author = {Mekyska, Jiří and Janoušová, Eva and GomezandVilda, Pedro and Smékal, Zdeněk and Rektorová, Irena and Eliášová, Ilona and Košťálová, Milena and Mračková, Martina and AlonsoandHernandez, Jesus B. and FaundezandZanuy, Marcos and LopezanddeandIpina, Karmele},
   article_location = {AMSTERDAM},
   article_number = {November},
   doi = {http://dx.doi.org/10.1016/j.neucom.2015.02.085},
   keywords = {Pathological speech; Disordered voice; Dysarthria; Speech processing; Bicepstrum; Non-linear dynamic features},
   language = {eng},
   issn = {0925-2312},
   journal = {Neurocomputing},
   title = {Robust and complex approach of pathological speech signal analysis},
   url = {http://ac.els-cdn.com/S0925231215007304/1-s2.0-S0925231215007304-main.pdf?_tid=f6afcc20-9991-11e5-a498-00000aacb35f&acdnat=1449128969_feb07c43c67d9cd575899e67b07d63bb},
   volume = {167},
   year = {2015}
}

TY  - JOUR
ID  - 1319700
AU  - Mekyska, Jiří - Janoušová, Eva - Gomez-Vilda, Pedro - Smékal, Zdeněk - Rektorová, Irena - Eliášová, Ilona - Košťálová, Milena - Mračková, Martina - Alonso-Hernandez, Jesus B. - Faundez-Zanuy, Marcos - Lopez-de-Ipina, Karmele
PY  - 2015
TI  - Robust and complex approach of pathological speech signal analysis
JF  - Neurocomputing
VL  - 167
IS  - November
SP  - 94-111
EP  - 94-111
PB  - ELSEVIER SCIENCE BV
SN  - 09252312
KW  - Pathological speech
KW  - Disordered voice
KW  - Dysarthria
KW  - Speech processing
KW  - Bicepstrum
KW  - Non-linear dynamic features
UR  - http://ac.els-cdn.com/S0925231215007304/1-s2.0-S0925231215007304-main.pdf?_tid=f6afcc20-9991-11e5-a498-00000aacb35f&acdnat=1449128969_feb07c43c67d9cd575899e67b07d63bb
L2  - http://ac.els-cdn.com/S0925231215007304/1-s2.0-S0925231215007304-main.pdf?_tid=f6afcc20-9991-11e5-a498-00000aacb35f&acdnat=1449128969_feb07c43c67d9cd575899e67b07d63bb
N2  - This paper presents a study of the approaches in the state-of-the-art in the field of pathological speech signal analysis with a special focus on parametrization techniques. It provides a description of 92 speech features where some of them are already widely used in this field of science and some of them have not been tried yet (they come from different areas of speech signal processing like speech recognition or coding). As an original contribution, this work introduces 36 completely new pathological voice measures based on modulation spectra, inferior colliculus coefficients, bicepstrum, sample and approximate entropy and empirical mode decomposition. The significance of these features was tested on 3 (English, Spanish and Czech) pathological voice databases with respect to classification accuracy, sensitivity and specificity. To our best knowledge the introduced approach based on complex feature extraction and robust testing outperformed all works that have been published already in this field. The results (accuracy, sensitivity and specificity equal to 100.0 +/- 0.0%) are discussable in the case of Massachusetts Eye and Ear Infirmary (MEEI) database because of its limitation related to a length of sustained vowels, however in the case of Principe de Asturias (PdA) Hospital in Alcala de Henares of Madrid database we made improvements in classification accuracy (82.1 +/- 3.3%) and specificity (83.8 +/- 5.1%) when considering a single-classifier approach. Hopefully, large improvements may be achieved in the case of Czech Parkinsonian Speech Database (PARCZ), which are discussed in this work as well. All the features introduced in this work were identified by Mann-Whitney U test as significant (p < 0.05) when processing at least one of the mentioned databases. The largest discriminative power from these proposed features has a cepstral peak prominence extracted from the first intrinsic mode function (p = 6.9443 x 10(-32)) which means, that among all newly designed features those that quantify especially hoarseness or breathiness are good candidates for pathological speech identification. The paper also mentions some ideas for the future work in the field of pathological speech signal analysis that can be valuable especially under the clinical point of view. (C) 2015 Elsevier B.V. All rights reserved.
ER  -

MEKYSKA, Jiří, Eva JANOUŠOVÁ, Pedro GOMEZ-VILDA, Zdeněk SMÉKAL, Irena REKTOROVÁ, Ilona ELIÁŠOVÁ, Milena KOŠŤÁLOVÁ, Martina MRAČKOVÁ, Jesus B. ALONSO-HERNANDEZ, Marcos FAUNDEZ-ZANUY a Karmele LOPEZ-DE-IPINA. Robust and complex approach of pathological speech signal analysis. \textit{Neurocomputing}. AMSTERDAM: ELSEVIER SCIENCE BV, 2015, roč.~167, November, s.~94-111. ISSN~0925-2312. Dostupné z: https://dx.doi.org/10.1016/j.neucom.2015.02.085.

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