Comparing 1-dimensional and 2-dimensional spectral feature representations in voice pathology detection using machine learning and deep learning classifiers
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A4 Artikkeli konferenssijulkaisussa
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Date
2022-09
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Language
en
Pages
5
2173 - 2177
2173 - 2177
Series
Proceedings of Interspeech'22, Interspeech
Abstract
The present study investigates the use of 1-dimensional (1-D) and 2-dimensional (2-D) spectral feature representations in voice pathology detection with several classical machine learning (ML) and recent deep learning (DL) classifiers. Four popularly used spectral feature representations (static mel-frequency cepstral coefficients (MFCCs), dynamic MFCCs, spectrogram and mel-spectrogram) are derived in both the 1-D and 2-D form from voice signals. Three widely used ML classifiers (support vector machine (SVM), random forest (RF) and Adaboost) and three DL classifiers (deep neural network (DNN), long short-term memory (LSTM) network, and convolutional neural network (CNN)) are used with the 1-D feature representations. In addition, CNN classifiers are built using the 2-D feature representations. The popularly used HUPA database is considered in the pathology detection experiments. Experimental results revealed that using the CNN classifier with the 2-D feature representations yielded better accuracy compared to using the ML and DL classifiers with the 1-D feature representations. The best performance was achieved using the 2-D CNN classifier based on dynamic MFCCs that showed a detection accuracy of 81%.Description
This work was supported by the Academy of Finland (grant number 313390). The computational resources were provided by Aalto ScienceIT.
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Javanmardi, F, Kadiri, S, Kodali, M & Alku, P 2022, Comparing 1-dimensional and 2-dimensional spectral feature representations in voice pathology detection using machine learning and deep learning classifiers . in INTERSPEECH 2022 . vol. 2022-September, Interspeech, International Speech Communication Association (ISCA), pp. 2173 - 2177, Interspeech, Incheon, Korea, Republic of, 18/09/2022 . https://doi.org/10.21437/Interspeech.2022-10420
