| Title |
Classification of speech signal using functional data / |
| Translation of Title |
Šnekos signalo klasifikavimas taikant funkcinius duomenis. |
| Authors |
Vengalienė, Judita |
| Full Text |
|
| Pages |
52 |
| Keywords [eng] |
Speech signal, gender classification, k-nearest neighbor, support vector machine, random forest. Šnekos signalas, lyties klasifikavimas, k-artimiausias kaimynas, atraminių vektorių mašina, atsitiktinis miškas. |
| Abstract [eng] |
The objective of this study is to classify Lithuanian words recorded in audio files by predicting the speaker's gender. Initially, the Hilbert transform was applied to the speech signals. Subsequently, after finding the optimal parameters, the smoothing of the speech signals was performed. Finally, the classification was done by using three classifiers: K-Nearest Neighbor, Support Vector Machine and Random Forest. All classifiers were applied to both functional and multivariate data after utilizing Functional Data Analysis. Evaluation of the results revealed that the Random Forest classifier for multivariate data was the most effective, achieving an accuracy of 82.60 % in predicting the speaker's gender. |
| Dissertation Institution |
Vilniaus universitetas. |
| Type |
Master thesis |
| Language |
English |
| Publication date |
2024 |
