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, roč. 167, November, s. 94-111. ISSN 0925-2312. doi:10.1016/j.neucom.2015.02.085. 2015.
Další formáty:   BibTeX LaTeX RIS
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
Originální 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
WWW 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
Změnil Změnila: Mgr. Eva Špillingová, učo 110713. Změněno: 4. 4. 2016 14:19.
Anotace
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 VaVNázev: CEITEC - central european institute of technology
NT13499, projekt VaVNázev: Řeč, její poruchy a kognitivní funkce u Parkinsonovy nemoci
VytisknoutZobrazeno: 29. 3. 2024 09:39