Zhenyu Liu

Orcid: 0000-0001-8401-9056

Affiliations:

Lanzhou University, School of Information Science and Engineering, Lanzhou, China
Lanzhou University, Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou, China

According to our database¹, Zhenyu Liu authored at least 31 papers between 2015 and 2024.

Collaborative distances:

Dijkstra number² of five.
Erdős number³ of four.

Timeline

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Bibliography

2024

Multi Fine-Grained Fusion Network for Depression Detection.

[BibT_eX]

[DOI]

ACM Trans. Multim. Comput. Commun. Appl., August, 2024

Stimulus-Response Patterns: The Key to Giving Generalizability to Text-Based Depression Detection Models.

[BibT_eX]

[DOI]

IEEE J. Biomed. Health Informatics, August, 2024

PIE: A Personalized Information Embedded model for text-based depression detection.

[BibT_eX]

[DOI]

Inf. Process. Manag., 2024

2023

Combining Informative Regions and Clips for Detecting Depression from Facial Expressions.

[BibT_eX]

[DOI]

Cogn. Comput., November, 2023

Depression recognition base on acoustic speech model of Multi-task emotional stimulus.

[BibT_eX]

[DOI]

Biomed. Signal Process. Control., August, 2023

PRA-Net: Part-and-Relation Attention Network for depression recognition from facial expression.

[BibT_eX]

[DOI]

Comput. Biol. Medicine, May, 2023

A radius-incorporated localized multiple kernel learning algorithm for detecting depression in speech.

[BibT_eX]

[DOI]

Int. J. Speech Technol., 2023

CAIINET: Neural network based on contextual attention and information interaction mechanism for depression detection.

[BibT_eX]

[DOI]

Digit. Signal Process., 2023

An Automatic Depression Detection Method with Cross-Modal Fusion Network and Multi-head Attention Mechanism.

[BibT_eX]

[DOI]

Proceedings of the Pattern Recognition and Computer Vision - 6th Chinese Conference, 2023

JAMFN: Joint Attention Multi-Scale Fusion Network for Depression Detection.

[BibT_eX]

[DOI]

Proceedings of the 24th Annual Conference of the International Speech Communication Association, 2023

A Visually Interpretable Convolutional-Transformer Model for Assessing Depression from Facial Images.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Multimedia and Expo, 2023

2022

Event-Based Bias Correction of the GPM IMERG V06 Product by Random Forest Method over Mainland China.

[BibT_eX]

[DOI]

Remote. Sens., 2022

2-level hierarchical depression recognition method based on task-stimulated and integrated speech features.

[BibT_eX]

[DOI]

Biomed. Signal Process. Control., 2022

2021

Undisturbed Mental State Assessment in the 5G Era: A Case Study of Depression Detection Based on Facial Expressions.

[BibT_eX]

[DOI]

IEEE Wirel. Commun., 2021

2020

A Novel Decision Tree for Depression Recognition in Speech.

[BibT_eX]

[DOI]

CoRR, 2020

MODMA dataset: a Multi-model Open Dataset for Mental-disorder Analysis.

[BibT_eX]

[DOI]

CoRR, 2020

A Behaviour Patterns Extraction Method for Recognizing Generalized Anxiety Disorder.

[BibT_eX]

[DOI]

Proceedings of the 22nd IEEE International Conference on E-health Networking, 2020

A Novel Bimodal Fusion-based Model for Depression Recognition.

[BibT_eX]

[DOI]

Proceedings of the 22nd IEEE International Conference on E-health Networking, 2020

Time-frequency Analysis Based on Hilbert-Huang Transform for Depression Recognition in Speech.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2020

2019

Deep Neural Networks for Depression Recognition Based on Facial Expressions Caused by Stimulus Tasks.

[BibT_eX]

[DOI]

Weitong Guo

Hongwu Yang

Zhenyu Liu

Proceedings of the 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, 2019

2018

Detecting Depression Using an Ensemble Logistic Regression Model Based on Multiple Speech Features.

[BibT_eX]

[DOI]

Comput. Math. Methods Medicine, 2018

A novel study for MDD detection through task-elicited facial cues.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2018

2017

Investigation of different speech types and emotions for detecting depression using different classifiers.

[BibT_eX]

[DOI]

Speech Commun., 2017

Detecting Depression in Speech Under Different Speaking Styles and Emotional Valences.

[BibT_eX]

[DOI]

Proceedings of the Brain Informatics - International Conference, 2017

Detecting depression in speech: Comparison and combination between different speech types.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine, 2017

Ensemble-based depression detection in speech.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine, 2017

Speech pause time: A potential biomarker for depression detection.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine, 2017

2016

Evaluation of Depression Severity in Speech.

[BibT_eX]

[DOI]

Proceedings of the Brain Informatics and Health - International Conference, 2016

Assessing stress levels via speech using three reading patterns.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2016

2015

Feature selection and classification of speech under long-term stress.

[BibT_eX]

[DOI]

Proceedings of the 2015 IEEE International Conference on Bioinformatics and Biomedicine, 2015

Detection of depression in speech.

[BibT_eX]

[DOI]

Proceedings of the 2015 International Conference on Affective Computing and Intelligent Interaction, 2015

Zhenyu Liu

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