Speech depression recognition based on attentional residual network

Abstract

Background: Depressive disorder is a common affective disorder, also known as depression, which is characterized by sadness, loss of interest, feelings of guilt or low self-worth and poor concentration. As speech is easy to obtain non-offensively with low-cost, many researchers explore the possibility of depression prediction through speech. Adopting speech signals to recognize depression has important practical significance. Aiming at the problem of the complex structure of the deep neural network method used in the recognition of speech depression and the traditional machine learning methods need to manually extract the features and the low recognition rate. Methods: This paper proposes a model that combines residual thinking and attention mechanism. First, depression corpus is designed based on the classic psychological experimental paradigm self-reference effect (SRE), and the speech dataset is labeled; then the attention module is introduced into the residual, and the channel attention is used to learn the features of the channel dimension, the spatial attention feedback the features of the spatial dimension, and the combination of the two to obtain the attention residual unit; finally the stacking unit constructs a speech depression recognition model based on the attention residual network. Results: Experimental results show that compared with traditional machine learning methods, this model obtains better results in the recognition of depression, which can meet the need for actual recognition application of depression. Conclusions: In this study, we not only predict whether person is depressed, but also estimate the severity of depression. In the designed corpus, the depression binary classification of an individual is given based on the severity of depression which is measured using BDI-II scores. Experimental results show that spontaneous speech can obtain better results than automatic speech, and the classification of speech features corresponding to negative questions is better than other tasks under negative emotions. Besides, the recognition accuracy rate of both male and female subjects is higher than that under other emotions.

Efficient Pause Extraction and Encode Strategy for Alzheimer’s Disease Detection Using Only Acoustic Features from Spontaneous Speech

“…Ditthapron et al [ 27 ] used smartphones to passively capture changes in acoustic characteristics of spontaneous speech for continuous traumatic brain injury monitoring. Spontaneous speech can also be used for research on depression [ 53 ] and aphasia [ 28 , 54 ]. Thus, our proposed spontaneous speech-based approach has the potential to be used in other clinical populations with acquired language disorders.…”

Emotion detection for supporting depression screening

“…To collect sensor data with sensors by using our tool presented in [ 14 ] would have required us to be in presence, because it is only a prototype, not easy to use. For this reason, we preferred to use pictures and speech in this preliminary version, reassured that speech is largely adopted in recent study on depression, see for example ([ 24 , 30 , 37 , 44 ]).…”

“…BDI-II score has been adopted to label the training dataset. Also He et al [ 24 ] used BDI-II scores for verifying their depression prediction when applying attentional residual network on Videos of the AVEC2013 and AVEC2014 datasets. The model also estimated the severity of depression.…”

Ensemble learning with speaker embeddings in multiple speech task stimuli for depression detection

“…Manual features such as spectral, source, prosodic, and formant features are commonly employed when analyzing depression and suicidality ( Cummins et al, 2015 ). Moreover, these features are also regarded as inputs to deep neural networks ( Lang and Cui, 2018 ; Lu X. et al, 2021 ). Studies have shown that the advanced features generated by MFCC feeding into the Short Long-Term Memory (LSTM) can preserve information related to depression ( Rejaibi et al, 2022 ).…”