Automated depression analysis using convolutional neural networks from speech.
To help clinicians to efficiently diagnose the severity of a person's depression, the affective computing community and the artificial intelligence field have shown a growing interest in designing automated systems. The speech features have useful information for the diagnosis of depression. However...
| Publicado en: | Journal of Biomedical Informatics Vol. 83; pp. 103 - 112 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Jul2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130691427&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130691427 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Jul2018 vid: 83 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 130691427 130691427 NLM29852317 130691427 10.1016/j.jbi.2018.05.007 NLM29852317 130691427 ppf: 103 ppct: 9 formats: tig: atl: Automated depression analysis using convolutional neural networks from speech. aug: au: He, Lang Cao, Cui affil: NPU-VUB joint AVSP Research Lab, School of Computer Science, Northwestern Polytechnical University (NPU), Xi’an, China sug: subj: Diagnosis, Computer Assisted Neural Networks (Computer) Depression Diagnosis Speech Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: To help clinicians to efficiently diagnose the severity of a person's depression, the affective computing community and the artificial intelligence field have shown a growing interest in designing automated systems. The speech features have useful information for the diagnosis of depression. However, manually designing and domain knowledge are still important for the selection of the feature, which makes the process labor consuming and subjective. In recent years, deep-learned features based on neural networks have shown superior performance to hand-crafted features in various areas. In this paper, to overcome the difficulties mentioned above, we propose a combination of hand-crafted and deep-learned features which can effectively measure the severity of depression from speech. In the proposed method, Deep Convolutional Neural Networks (DCNN) are firstly built to learn deep-learned features from spectrograms and raw speech waveforms. Then we manually extract the state-of-the-art texture descriptors named median robust extended local binary patterns (MRELBP) from spectrograms. To capture the complementary information within the hand-crafted features and deep-learned features, we propose joint fine-tuning layers to combine the raw and spectrogram DCNN to boost the depression recognition performance. Moreover, to address the problems with small samples, a data augmentation method was proposed. Experiments conducted on AVEC2013 and AVEC2014 depression databases show that our approach is robust and effective for the diagnosis of depression when compared to state-of-the-art audio-based methods. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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