| Autor: |
Hazmoune, Samira, Bougamouza, Fateh, Mazouzi, Smaine, Benmohammed, Mohamed |
| Zdroj: |
International Journal of Intelligent Systems Technologies and Applications; 2024, Vol. 22 Issue: 1 p41-76, 36p |
| Abstrakt: |
In this paper, we propose an ensemble method based on hidden Markov models (HMMs) for speech recognition. Our objective is to reduce the impact of the initial setting of training parameters on the final model while improving accuracy and robustness, particularly in speaker independent systems. The main idea is to exploit the sensitivity of HMMs to the initial setting of training parameters, thus creating diversity among the ensemble members. Additionally, we perform an experimental study to investigate the potential relationship between initial training parameters and ten diversity measures from literature. The proposed method is assessed on a standard dataset from the UCI machine-learning repository. Results demonstrate its effectiveness in terms of accuracy and robustness to intra-class variability, surpassing basic classifiers (HMM, KNN, NN, SVM) and some previous works in the literature including those using deep learning algorithms such as convolutional neural networks (CNNs) and long short-term memory (LSTM). |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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