Depression automatic evaluation system and method based on phonetic features and machine learning

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...

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

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