| Autor: |
Barkani, Fatima, Hamidi, Mohamed, Zealouk, Ouissam, Satori, Hassan |
| Předmět: |
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| Zdroj: |
International Journal of Online & Biomedical Engineering; 2023, Vol. 19 Issue 12, p49-61, 13p |
| Abstrakt: |
This paper introduces an innovative technique for creating a cough detection system that relies on speech recognition algorithms. The strategy utilizes the Kaldi platform, which is open source and incorporates a hybrid system of Gaussian Mixture Model-based Hidden Markov Models (GMM-HMM) through a straightforward monophone training model. Additionally, the study examines the effectiveness of two different feature extraction approaches, Mel Frequency Cepstral Coefficient (MFCC) and Perceptual Linear Prediction (PLP). The proposed system can function as a collection tool for gathering natural and spontaneous cough data from conversations or continuous speech. The paper also compares the Kaldi and CMU Sphinx4 toolkits, concluding that Kaldi's use of GMM-HMM outperforms CMU Sphinx4. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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