Name
Title | ||
|---|---|---|
Ensemble Support Vector Machine and Neural Network Method for Speech Stress Recognition |
Abstract | ||
|---|---|---|
This paper has proposed a system that can be analyzed of speech stress recognition. The proposed method (ensemble SVM and NN) is analyzed comparatively proven to have high accuracy. The ensemble method has been applied to improve machine learning ability in identifying with a small number of datasets. It is caused; due to stress is one of the unconscious emotions. Stress can be recognized by speech however it is not robust. It was caused by small datasets. In this work, we use the sample of SUSAS dataset. The dataset is divided into 10 groups by the combinational method. Each group of data trained using SVM then combined it with trained NN. The experimental results show that with a small dataset, the proposed method outperformed the previous methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IWBIS.2018.8471698 | 2018 International Workshop on Big Data and Information Security (IWBIS) |
Keywords | Field | DocType |
ensemble,stress recognition,speech,machine learning,deep learning,neural network,support vector machine | Small number,Kernel (linear algebra),Pattern recognition,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Stress recognition,Artificial neural network | Conference |
ISBN | Citations | PageRank |
978-1-5386-5526-9 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Barlian Henryranu Prasetio | 1 | 0 | 1.35 |
| Hiroki Tamura | 2 | 72 | 21.29 |
| Koichi Tanno | 3 | 57 | 22.05 |