A radius-incorporated localized multiple kernel learning algorithm for detecting depression in speech

Haihua Jiang*, Bin Hu*, Zhenyu Liu, Gang Wang, Lan Zhang

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

摘要

Early intervention for depression could provide a means to reducing the disease burden, but there is a lack of objective diagnostic methods. This study investigated automatic depression classification on a speech dataset of 85 healthy controls (51 females and 34 males) and 85 depressed patients (53 females and 32 males). Considering that there are obvious differences in the performance of different types of speech features, we propose a radius-incorporated localized multiple kernel learning (trLMKL) algorithm for detecting depression in speech to make the best use of speech features. To improve the classification accuracy, we combine the information of both the margin and the radius of the MEB to learn the gating model parameters in our algorithm. Furthermore, we do not directly incorporate the radius of the MEB, but incorporate the trace of the total scattering matrix of training data. This method can avoid the time cost of calculating the radius at each iteration and decrease the computational complexity. Comprehensive experiments were carried out on our depressed speech dataset and 10 UCI datasets. Our algorithm achieved better classification performance overall than SimpleMKL and LMKL, and it was efficient at detecting depression, indicating its potential for use as a diagnostic method for depression.

源语言英语
页(从-至)371-378
页数8
期刊International Journal of Speech Technology
26
2
DOI
出版状态已出版 - 7月 2023
已对外发布

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