基于跨用户语音域适应网络的抑郁症检测

doi:10.16180/j.cnki.issn1007-7820.2025.01.012

Abstract

Abstract:

Because of the subjective detection of depression, the use of user voice diagnosis of depression has become a more potential auxiliary way. However, the speech signals of different users are different. In this study, a CUADAN(Cross User Audio Domain Adaptation Network) is proposed to detect depression. Visual Mel spectrograms are extracted from the audio, and the feature extractor of the CUADAN model is used to extract deeper depression features from the Mel spectrograms. Since the source domain and target domain contain the voice features of different healthy users and depressed users, the domain classifier of CUADAN model is used to perform domain adaptation between different user data, so that unknown users can be detected by existing classifiers. The experimental results show that the CUADAN model has a higher depression detection accuracy, with an average accuracy of 81.0±2.4%. Therefore, the CUADAN model can effectively weaken the differences between different users' voices and improve the accuracy of cross-user depression detection.

Key words: domain adaptation, depression detection, CUADAN, audio, cross-user, Mel spectrogram, feature extraction, weakening differences

CLC Number:

TP181

WU Wei, MA Longhua, ZHAO Xianghong. Depression Detection Based on Cross User Audio Domain Adaptation Network[J].Electronic Science and Technology, 2025, 38(1): 88-94.

Figures/Tables 7

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References 20

[1]	Fried E I, Flake J K, Robinaugh D J. Revisiting the theoretical and methodological foundations of depression measurement[J]. Nature Reviews Psychology, 2022, 1(6):358-368. doi: 10.1038/s44159-022-00050-2 pmid: 38107751
[2]	陈方方, 吴明飞, 张佳俐. 认知情绪调节策略对青少年单相抑郁和双相抑郁的影响[J]. 中华行为医学与脑科学杂志, 2022, 31(12):1086-1091.
	Chen Fangfang, Wu Mingfei, Zhang Jiali. The impact of cognitive emotion regulation strategies on unipolar and bipolar depression in adolescents[J]. Chinese Journal of Behavioral Medicine and Brain Science, 2022, 31(12):1086-1091.
[3]	Dubovsky S L, Ghosh B M, Serotte J C, et al. Psychotic depression:Diagnosis,differential diagnosis and treatment[J]. Psychotherapy and Psychosomatics, 2021, 90(3):160-177. doi: 10.1159/000511348 pmid: 33166960
[4]	Carrozzino D, Patierno C, Fava G A, et al. The hamilton rating scales for depression:A critical review of clinimetric properties of different versions[J]. Psychotherapy and Psychosomatics, 2020, 89(3):133-150. doi: 10.1159/000506879 pmid: 32289809
[5]	Ubukata S, Ueda K, Fujimoto G, et al. Extracting apathy from depression syndrome in traumatic brain injury by using a clustering method[J]. The Journal of Neuropsychiatry and Clinical Neurosciences, 2022, 34(2):158-167.
[6]	Stasak B, Joachim D, Epps J. Breaking age barriers with automatic voice-based depression detection[J]. IEEE Pervasive Computing, 2022, 21(2):10-19.
[7]	Haick H, Tang N. Artificial intelligence in medical sensors for clinical decisions[J]. ACS Nano, 2021, 15(3):3557-3567. doi: 10.1021/acsnano.1c00085 pmid: 33620208
[8]	Huang Z, Epps J, Joachim D, et al. Natural language processing methods for acoustic and landmark event-based features in speech-based depression detection[J]. IEEE Journal of Selected Topics in Signal Processing, 2019, 14(2):435-448.
[9]	Calić G, Petrović-Lazić M, Mentus T, et al. Acoustic features of voice in adults suffering from depressio[J]. Psihološka Istraživanja, 2022, 25(2):183-203.
[10]	Ding Y, Zhong S, Hua L. Automatic recognition of student emotions based on deep neural network and its application in depression detection[J]. Journal of Medical Imaging and Health Informatics, 2020, 10(11):2634-2641.
[11]	Zhuang F, Qi Z, Duan K, et al. A comprehensive survey on transfer learning[J]. Proceedings of the IEEE, 2020, 109(1):43-76.
[12]	Long M, Cao Y, Wang J, et al. Learning transferable features with deep adaptation networks[C]. Lille: International Conference on Machine Learning, 2015:97-105.
[13]	Cai H, Yuan Z, Gao Y, et al. A multi-modal open dataset for mental-disorder analysis[J]. Scientific Data, 2022, 9(1):178-178. doi: 10.1038/s41597-022-01211-x pmid: 35440583
[14]	Nguyen M T, Lin W W, Huang J H. Heart sound classification using deep learning techniques based on Log-Mel spectrogram[J]. Circuits,Systems and Signal Processing, 2023, 42(1):344-360.
[15]	Park J, Moon N. Design and implementation of attention depression detection model based on multimodal analysis[J]. Sustainability, 2022, 14(6):1-15.
[16]	詹雁, 张娟. 一种结构感知损失的域适应深度估计方法[J]. 电子科技, 2020, 33(12):12-16.
	Zhan Yan, Zhang Juan. A domain adaptive depth estimation method for structural perception loss[J]. Electronic Science and Technology, 2020, 33(12):12-16.
[17]	Finjan R H, Rasheed A S, Hashim A A, et al. Arabic handwritten digits recognition based on convolutional neural networks with ResNet-34 model[J]. Indonesian Journal of Electrical Engineering and Computer Science, 2021, 21(1):174-178.
[18]	林潮威, 李菲菲, 陈虬. 基于深度卷积特征的场景全局与局部表示方法[J]. 电子科技, 2022, 35(4):20-27.
	Lin Chaowei, Li Feifei, Chen Qiu. Globaland local scene representation method based on deep convolutional features[J]. Electronic Science and Technology, 2022, 35(4):20-27.
[19]	Ganin Y, Ustinova E, Ajakan H, et al. Domain-adversarial training of neural networks[J]. The Journal of Machine Learning Research, 2016, 17(1):2096-2030.
[20]	Kobak D, Linderman G C. Initialization is critical for preserving global data structure in both t-SNE and UMAP[J]. Nature Biotechnology, 2021, 39(2):156-157.

特征	抑郁组	健康组
人数(男∶女)	16∶7	20∶9
年龄	16~56	18~55
接受教育年数	6~19	12~19

模型	第1组 /%	第2组 /%	第3组 /%	第4组 /%	平均准确率及标准差/%
SVM	67.2	57.5	48.9	65.8	59.8±7.3
LR	63.2	44.8	57.4	60.7	56.5±7.1
DenseNet	71.6	63.5	68.4	70.8	68.6±3.2
ConvLSTM	74.7	67.8	69.2	71.7	70.9±2.6
CUADAN	84.4	78.9	78.6	82.2	81.0±2.4

Depression Detection Based on Cross User Audio Domain Adaptation Network

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