Voice signal-based gradient boosting decision tree depression identification method

Abstract

The invention relates to a voice signal-based gradient boosting decision tree depression identification method. The method comprises the following steps: obtaining voice data of an interviewer and a corresponding PHQ-8 depression screening scale score, enabling a voice signal to correspond to a PHQ-8 value, selecting a training sample set for training, and testing the sample set; extracting prosodic features representing emotion and depression in the voice signal, and spectrum-based related features and tone quality features; and performing learning on the training set by adopting a machine learning method of a gradient boosting decision tree, and taking the PHQ-8 score as an output result as a basis for judging the depression degree. According to the invention, by using the gradient boosting decision tree as a learning method, the accuracy of the predicted PHQ-8 value and the timeliness of training are improved, the PHQ-8 value of the PHQ-8 depression screening scale is taken as an output result, the score of the PHQ-8 value is between 0 and 24, the depression is determined when the score is higher than 10 and lower than 20, and the severe depression is determined when the score is higher than 20. The method has higher accuracy and objectivity.

Description

technical field [0001] The invention relates to the field of depression recognition in affective computing, in particular to a speech signal-based gradient lifting decision tree depression recognition method. Background technique [0002] In recent years, with the development of artificial intelligence and robot technology, the traditional human-computer interaction mode can no longer meet the needs, and the new human-computer interaction requires emotional communication. Therefore, emotion recognition has become the key to the development of human-computer interaction technology and has become A hot research topic in the academic circle. Emotion recognition is a multidisciplinary research topic. By enabling computers to understand and recognize human emotions, and then predict and understand human behavior trends and psychological states, efficient and harmonious human-computer emotional interaction can be achieved. [0003] Depression is a mental state of low mood and ave...

AI Technical Summary

Problems solved by technology

Many depressed patients will hide their true inner thoughts, so that these scales often cannot objectively reflect the real situation of patients

Due to the lack of objective methods for diagnosis, psychologists need to conduct interviews with patients, and use their own knowledge to diagnose patients' speech, actions, and emo

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