Depression Recognition Based on Speech Corpus Alignment and Adaptive Fusion

Depression has become a significant global public health issue.Speech-based depression recognition aims to recognize depression in an easily scalable and cost-effective manner.Prior studies often divide long speech into multiple slices,and optimize models with them independently or further establish their relationship via temporal modules.They fail to make the most of the intra- and inter-relationships between segmented speech,concurrently introducing some task-irrelevant information.This paper proposes a depression recognition method based on speech corpus alignment and adaptive fusion.After segmenting the input speech,multi-granularity feature correlation is established through a multi-head attention mechanism,and the segment importance mining module is used to automatically learn the importance of different segments.This method effectively integrates local and global features,significantly improving recognizing performance.The proposed method achieves a weighted accuracy of 82.59%,an unweighted accuracy of 82.17%,and an F1 score of 82.23%,respectively,on the MODMA database.On the SEARCH database,the weighted accuracy,unweighted accuracy,and F1 score are 74.44%,68.33%,and 69.25%,respectively.The experiments demonstrate that the proposed model can accurately recognize depression,outperforming existing works.