Acoustic investigation of speech pathologies based on the discriminative paraconsistent machine (DPM)

Author(s)

Fonseca, Everthon Silva ^{[1, 2]} ; Guido, Rodrigo Capobianco ^[1] ; Barbon Junior, Sylvio ^[3] ; Dezani, Henrique ^[4] ; Gati, Rodrigo Rosseto ^[3] ; Mosconi Pereira, Denis Cesar ^[3]

Affiliation

^[1] Sao Paulo State Univ, Univ Estadual Paulista, UNESP, Inst Biociencias Letras & Ciencias Exatas, Rua Cristovao Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP - Brazil

^[2] Sao Paulo Fed Inst, IFSP, Dept Ind & Automat, Catanduva, SP - Brazil

^[3] Univ Estadual Londrina, UEL, Comp Sci Dept, Londrina, PR - Brazil

^[4] Sao Paulo State Technol Coll, FATEC, Sao Jose Do Rio Preto, SP - Brazil

• Document type: Journal article
• Source: Biomedical Signal Processing and Control; v. 55, JAN 2020.
• Web of Science Citations: 3

Abstract

Background: Voice disorders are related to both modest and severe health problems, including discomfort, pain, difficulty speaking, dysphagia and also cancer. Widely adopted worldwide, the combined invasive and subjective diagnosis of voice disorders is troublesome and error-prone. Contrarily, acoustic-based digital assessment allows for a non-intrusive and objective examination, stimulating the applications of computer-based tools. Objective: Consequently, this work describes a novel algorithm to investigate speech pathologies from the sounds of sustained vowels, particularly exploring a potential gap: the classification of co-existent issues for which the major phonic symptom is the same, implying in similar inter-class features. Method: By using the concepts of signal energy (SE), zero-crossing rates (ZCRs) and signal entropy (SH), which provide a joint time-frequency-information map, the proposed approach classifies voice signals based on the discriminative paraconsistent machine (DPM), allowing for the application of paraconsistency to treat indefinitions and contradictions. Results: An accuracy level of 95% was obtained under a subset of voices from the Saarbrucken voice database (SVD), with just a modest training. In complement, the proposed approach offers wider possibilities in contrast to current state-of-the-art systems, allowing for the inputs to be mapped into the paraconsistent plane in such a way that intermediary states can be found. Conclusion: Different from current algorithms, our technique focuses on a particular problem in the field of speech pathology detection (SPD), not yet explored in detail, proposing a way to successfully solve it. Furthermore, the results we obtained stimulate broaden studies involving speech data inconsistencies whilst providing a valid contribution. (C) 2019 Elsevier Ltd. All rights reserved. (AU)

• FAPESP's process: 19/04475-0 - Paraconsistent Feature Analysis of Speech Signals: fighting the voice spoofing attacks
• Grantee: Rodrigo Capobianco Guido
• Support Opportunities: Regular Research Grants