MEKYSKA, Jiří, Eva JANOUŠOVÁ, Pedro GOMEZ-VILDA, Zdeněk SMÉKAL, Irena REKTOROVÁ, Ilona ELIÁŠOVÁ, Milena KOŠŤÁLOVÁ, Martina MRAČKOVÁ, Jesus B. ALONSO-HERNANDEZ, Marcos FAUNDEZ-ZANUY and Karmele LOPEZ-DE-IPINA. Robust and complex approach of pathological speech signal analysis. Neurocomputing. AMSTERDAM: ELSEVIER SCIENCE BV, 2015, vol. 167, November, p. 94-111. ISSN 0925-2312. doi:10.1016/j.neucom.2015.02.085.
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Basic information
Original name Robust and complex approach of pathological speech signal analysis
Authors MEKYSKA, Jiří (203 Czech Republic), Eva JANOUŠOVÁ (203 Czech Republic, belonging to the institution), Pedro GOMEZ-VILDA (724 Spain), Zdeněk SMÉKAL (203 Czech Republic), Irena REKTOROVÁ (203 Czech Republic, guarantor, belonging to the institution), Ilona ELIÁŠOVÁ (203 Czech Republic, belonging to the institution), Milena KOŠŤÁLOVÁ (203 Czech Republic, belonging to the institution), Martina MRAČKOVÁ (203 Czech Republic, belonging to the institution), Jesus B. ALONSO-HERNANDEZ (724 Spain), Marcos FAUNDEZ-ZANUY (724 Spain) and Karmele LOPEZ-DE-IPINA (724 Spain).
Edition Neurocomputing, AMSTERDAM, ELSEVIER SCIENCE BV, 2015, 0925-2312.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Netherlands
Confidentiality degree is not subject to a state or trade secret
Impact factor Impact factor: 2.392
RIV identification code RIV/00216224:14740/15:00085054
Organization unit Central European Institute of Technology
UT WoS 000358808500012
Keywords in English Pathological speech; Disordered voice; Dysarthria; Speech processing; Bicepstrum; Non-linear dynamic features
Tags podil, rivok
Tags International impact, Reviewed
Changed by Changed by: Mgr. Eva Špillingová, učo 110713. Changed: 4. 4. 2016 14:19.
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.
ED1.1.00/02.0068, research and development projectName: CEITEC - central european institute of technology
NT13499, research and development projectName: Řeč, její poruchy a kognitivní funkce u Parkinsonovy nemoci
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