| Autor: |
Zuzana Dankovičová, Dávid Sovák, Peter Drotár, Liberios Vokorokos |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
|
| Zdroj: |
Applied Sciences, Vol 8, Iss 10, p 1927 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
2076-3417 |
| DOI: |
10.3390/app8101927 |
| Popis: |
This paper addresses the processing of speech data and their utilization in a decision support system. The main aim of this work is to utilize machine learning methods to recognize pathological speech, particularly dysphonia. We extracted 1560 speech features and used these to train the classification model. As classifiers, three state-of-the-art methods were used: K-nearest neighbors, random forests, and support vector machine. We analyzed the performance of classifiers with and without gender taken into account. The experimental results showed that it is possible to recognize pathological speech with as high as a 91.3% classification accuracy. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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