Autor(es):
Fonseca, Everthon Silva [UNESP] ; Guido, Rodrigo Capobianco [UNESP] ; Junior, Sylvio Barbon ; Dezani, Henrique ; Gati, Rodrigo Rosseto ; Mosconi Pereira, Denis César
Data: 2020
Identificador Persistente: http://hdl.handle.net/11449/201174
Origem: Oasisbr
Assunto(s): Co-existent voice disorders; Discriminative paraconsistent machine (DPM); Overlapped inter-class features; Signal energy (SE); Signal entropy (SH); Zero-crossing rate (ZCR); Co-existent voice disorders; Co-existent voice disorders; Discriminative paraconsistent machine (DPM); Discriminative paraconsistent machine (DPM); Overlapped inter-class features; Overlapped inter-class features; Signal energy (SE); Signal energy (SE); Signal entropy (SH); Signal entropy (SH); Zero-crossing rate (ZCR); Zero-crossing rate (ZCR)
Descrição
Made available in DSpace on 2020-12-12T02:25:59Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-01-01
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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.
Instituto de Biociências Letras e Ciências Exatas Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo 2265, Jd Nazareth, 15054-000
IFSP - São Paulo Federal Institute Department of Industry and Automation
UEL - Londrina State University Computer Science Department
FATEC - São Paulo State Technology College
Instituto de Biociências Letras e Ciências Exatas Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo 2265, Jd Nazareth, 15054-000
