Szczegóły publikacji
Opis bibliograficzny
Speaker identification based on artificial neural networks : case study: the Polish vowel $\alpha$ (pilot study) / Kinga Salapa, Agata Trawińska, Irena Roterman, Ryszard TADEUSIEWICZ // Bio-Algorithms and Med-Systems / Jagiellonian University. Medical College ; ISSN 1895-9091. — 2014 — vol. 10 iss. 2, s. 91–99. — Bibliogr. s. 98–99, Abstr.
Autorzy (4)
- Salapa Kinga
- Trawińska Agata
- Roterman Irena
- AGHTadeusiewicz Ryszard
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 81804 |
|---|---|
| Data dodania do BaDAP | 2014-06-11 |
| DOI | 10.1515/bams-2014-0006 |
| Rok publikacji | 2014 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Bio-Algorithms and Med-Systems |
Abstract
Current statistical methods and technologies used for speaker identification via dynamic formant frequency often involve classic multivariate analyses that must meet a number of criteria in order to be considered trustworthy. The authors propose more advanced classification techniques, including artificial neural networks. Owing to iterative learning algorithms, neural networks can be trained to detect highly complex, nonlinear relations hidden in input data. This study specifically considers feed-forward multilayer perceptron and radial basic function network models. The investigation involves an analysis of the Polish vowel (stressed or unstressed) in selected contexts described by the four lowest formant frequencies. Results indicate high accuracy of neural networks as a speaker identification tool reaching up to 100%. In addition, the authors have determined that the accuracy of classification is similar when based on a single context to when input data are aggregated over several different contexts.
