![]() People suffering Bipolar Disorder (BD) experiment changes in mood status having depressive or manic episodes with normal periods in the middle. The results also showed that our method had better robustness on gender effect. Compared with baseline method and bagging classification, the highest accuracy of our method was raised by 9.62% and 9.49% respectively in gender-independent experiments, and F1 score also got improvement obviously. The experimental results showed that the proposed method had achieved good results in both gender-independent and gender-dependent experiments. Our experiments were based on the depression speech database of the Gansu Provincial Key Laboratory of Wearable Computing. The final result was achieved on new features by SVM. In the stage of 2nd-level classification, the results of tasks with significant accuracy differences were aggregated into new integrated features. Then, support vector machine (SVM) and random forest (RF) were used to obtain primary results. In 1st-level classification stage, i-vectors were extracted based on spectral features, prosodic features, formants and voice quality of speech segments in different task stimulus respectively. In order to solve these problems, a novel 2-level hierarchical depression recognition method was proposed in this paper. However, the problems such as the speech variation in different emotional stimulus, gender impact, the speaker and channel variation and the variable length of frame feature, would have a great impact on recognition performance. Speech depression recognition had become a research hotspot due to its advantages of non-invasiveness and easy access to data. Depression had been paid more and more attention by researchers because of its high prevalence, recurrence, disability and mortality. ![]()
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