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Depression automatic assessment system and method based on speech features and machine learning

A speech feature and machine learning technology, applied in sensors, medical science, psychological devices, etc., can solve problems such as lack of diagnosis and curative effect evaluation, difficulty in identifying emotional disorders, and patients losing opportunities for treatment, etc.

Active Publication Date: 2018-04-24
LANZHOU UNIVERSITY
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  • Summary
  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, the diagnosis and efficacy evaluation of depression lack objective measurement methods and tools, and mainly rely on subjective evaluation methods such as family history, patient self-report, and clinical scales, which makes it difficult to identify early emotional disorders, and patients often lose the best opportunity for treatment

Method used

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  • Depression automatic assessment system and method based on speech features and machine learning
  • Depression automatic assessment system and method based on speech features and machine learning
  • Depression automatic assessment system and method based on speech features and machine learning

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Embodiment Construction

[0028] Embodiments of the present invention will be described in further detail below in conjunction with the accompanying drawings.

[0029] figure 1 It is a schematic diagram of the general framework of the system of the present invention. An automatic evaluation system for depression based on speech features and machine learning, including (a) a voice collection and recording module, which is used to record the voice information of the subject while stimulating the subject with different emotional corpus; (b ) voice feature calculation module, used to calculate the acoustic features of voice after preprocessing the voice signal collected; (c) voice database module, including the correlation data used for voice feature validity analysis, classifier training optimization, and system classification rate data; (d) the machine learning module, which uses the data of the speech database to carry out the correlation analysis between speech features and depression, so as to determ...

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Abstract

The invention provides a depression automatic evaluation system and method based on phonetic features and machine learning. On the basis of speech processing, feature extracting and machine learning technologies, the system and method search for the relations between phonetic features and depression, and provide objective references for clinical diagnosis of depression. The system includes 1, a speech acquisition recording module for recording speech information of testees at different emotional and communication stimulation; 2, a speech feature calculating module for calculating acoustic features of the speech, 3, a speech database module containing relative data for speech feature efficiency analysis, classifier training optimization and a system classification rate; 4, a machine learning module for determining an extracting mode of effective features and training a classifier for automatic evaluation; 5; an automatic evaluation module. The system and method conduct depression degree classification on the effective speech features of the testees according to the extracting mode of the effective features and a classification mode of the trained classifier.

Description

technical field [0001] The invention relates to the technical field of computer-aided medical diagnosis, in particular to an automatic assessment system and method for depression based on speech features and machine learning. Background technique [0002] Depression is a common mental illness that affects about 350 million people around the world. The World Health Organization (WHO) predicts that depression will become the second leading cause of disease in the world by 2020, so finding an effective assessment An approach to depression is necessary. At present, the diagnosis and efficacy evaluation of depression lack objective measurement methods and tools, and mainly rely on subjective evaluation methods such as family history, patient self-report, and clinical scales, which makes it difficult to identify early emotional disorders, and patients often lose the best opportunity for treatment . [0003] With the development of speech signal analysis and processing technology...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/16
CPCA61B5/165
Inventor 胡斌刘振宇康环宇
Owner LANZHOU UNIVERSITY
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