Automatic depression recognition by intelligent speech signal processing: A systematic survey

Wu, Pingping; Wang, Ruihao; Lin, Hong; Zhang, Fanlong; Tu, Juan; Sun, Miao

doi:10.1049/cit2.12113

Cited by 42 publications

(12 citation statements)

References 99 publications

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“…With the fast development of deep learning (DL), various approaches have recently been applied to SER with considerable performance gains [9][10][11][12]. In addition, other studies [13,14] have contributed to the research on the aforementioned challenges.…”

mentioning

confidence: 99%

DeepCNN: Spectro‐temporal feature representation for speech emotion recognition

Saleem

Gao

Irfan

et al. 2023

CAAI Trans on Intel Tech

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Speech emotion recognition (SER) is an important research problem in human-computer interaction systems. The representation and extraction of features are significant challenges in SER systems. Despite the promising results of recent studies, they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive fields. To mitigate this problem, this article proposes DeepCNN, which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks (CNNs) and a convolution layer-based transformer. Two parallel CNNs are applied to extract the spectral features (2D-CNN) and temporal features (1D-CNN) representations. A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel CNNs. The learnt low-level concatenated features are then applied to a deep framework of convolutional blocks, which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification layer. This fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature map. The Berlin Database of Emotional Speech (EMO-BD) and Interactive Emotional Dyadic Motion Capture (IEMOCAP) datasets are used in experiments to recognise distinct speech emotions. With efficient spectral and temporal feature representation, the proposed SER model achieves 94.2% accuracy for different emotions on the EMO-BD and 81.1% accuracy on the IEMOCAP dataset respectively. The proposed SER system, DeepCNN, outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

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mentioning

confidence: 99%

DeepCNN: Spectro‐temporal feature representation for speech emotion recognition

Saleem

Gao

Irfan

et al. 2023

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…Some authors also conducted a review study on depression detection. For instance, Wu et al ( 2022 ) conducted a similar review study on depression detection using speech signals. While the authors reported that there has been a shift from exploring auditory features to deep model for speech depression recognition, they recommended overcoming depression detection challenges by collecting clinical information on depression to explore the core mechanism of speech in depression.…”

Section: Discussionmentioning

confidence: 99%

“…They also concluded that combining multiple modalities for accurate and effective depression analysis is a possible trend in future research. While the review study by Wu et al ( 2022 ) is also on depression detection, the authors focus more on employing deep learning methods with speech signals for depression detection, a contrast to this review study, which focuses on machine learning and deep learning models using various types of datasets for depression detection. We also quite differently propose the feature fusion method for future work.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review

et al. 2022

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Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are increasing in some countries, such as the US. Suicide prevention is a challenging global public health problem. Currently, there aren’t any validated clinical biomarkers for suicidal diagnosis, and traditional methods exhibit limitations. Artificial intelligence (AI) is budding in many fields, including in the diagnosis of medical conditions. This review paper summarizes recent studies (past 8 years) that employed AI tools for the automated detection of depression and/or anxiety disorder and discusses the limitations and effects of some modalities. The studies assert that AI tools produce promising results and could overcome the limitations of traditional diagnostic methods. Although using AI tools for suicidal ideation exhibits limitations, these are outweighed by the advantages. Thus, this review article also proposes extracting a fusion of features such as facial images, speech signals, and visual and clinical history features from deep models for the automated detection of depression and/or anxiety disorder in individuals, for future work. This may pave the way for the identification of individuals with suicidal thoughts.

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“…Recently, there has been growing interest in using arti cial intelligence (AI) to detect depression from a combination of text and audio features and answers of the Patient Health Questionnaire-8 (PHQ-8) survey. A study by [2] used a combination of audio, text, and PHQ-8 features to develop a multimodal deep learning model that achieved high classi cation accuracy in detecting depression. There is ample evidence [3,5] for text-based approach and have achieved high performance in detecting depression.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Depression Detection through Advanced Text Analysis: Integrating BERT, Autoencoder, and LSTM Models

Firoz

Beresteneva

Vladimirovich

et al. 2023

Preprint

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Depression is a austere medical ailment that upsets numerous people worldwide, causing a persistent decrease in mood and significantly impacting their emotions. The article focuses on utilizing BERT techniques and Autoencoders to detect depression from text data, considering gender differences. The work stresses on feature engineering of text data provided by benchmark dataset DAIC_WOZ. We experiment with BERT embeddings that encodes the meaning of text to derive text features. They are then fused with the help of Autoencoders with other parametric features from PHQ-8 survey responses, absolutist word count and gender information. The study found that incorporating this information significantly enhances the functioning of the model. The proposed method outperformed the baseline models. We emphasize the potential of machine learning for mental health research that considers gender differences. We report 98.6% accuracy demonstrated by our method. We found the mean absolute error (MAE) as 0.19 and root mean squared error (RMSE) as 0.282 which signifies the high performance of our proposed method for binary depression classification.

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Automatic depression recognition by intelligent speech signal processing: A systematic survey

Cited by 42 publications

References 99 publications

DeepCNN: Spectro‐temporal feature representation for speech emotion recognition

DeepCNN: Spectro‐temporal feature representation for speech emotion recognition

Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review

Enhancing Depression Detection through Advanced Text Analysis: Integrating BERT, Autoencoder, and LSTM Models

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