# Prediction of Depression Severity Based on the Prosodic and Semantic Features with Bidirectional LSTM and Time Distributed CNN

arXiv: https://arxiv.org/abs/2202.12456 · HTML: https://mlsanity.com/paper/2202.12456/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 74 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [Depression and Self-Harm Risk Assessment in Online Forums](https://mlsanity.com/paper/1709.01848/) (2017)
- [Forecasting the onset and course of mental illness with Twitter data](https://mlsanity.com/paper/1608.07740/) (2016)
- [Multi-level Attention network using text, audio and video for Depression Prediction](https://mlsanity.com/paper/1909.01417/) (2019)

## Built on by (most cited first, 7 of 7)

- [LMVD: A Large-Scale Multimodal Vlog Dataset for Depression Detection in the Wild](https://mlsanity.com/paper/2407.00024/) (2024)
- [Depression Detection and Analysis using Large Language Models on Textual and Audio-Visual Modalities](https://mlsanity.com/paper/2407.06125/) (2024)
- [Fair Uncertainty Quantification for Depression Prediction](https://mlsanity.com/paper/2505.04931/) (2025)
- [Explainable Multimodal Depression Recognition in Clinical Interviews via PHQ-Aligned Symptom Summarization](https://mlsanity.com/paper/2501.16106/) (2025)
- [Multi-aspect Depression Severity Assessment via Inductive Dialogue System](https://mlsanity.com/paper/2410.21836/) (2024)
- [RBA-FE: A Robust Brain-Inspired Audio Feature Extractor for Depression Diagnosis](https://mlsanity.com/paper/2506.07118/) (2025)
- [Towards a Unified Textual Graph Framework for Spectral Reasoning via Physical and Chemical Information Fusion](https://mlsanity.com/paper/2506.17761/) (2025)

## Abstract (arXiv)

Depression is increasingly impacting individuals both physically and psychologically worldwide. It has become a global major public health problem and attracts attention from various research fields. Traditionally, the diagnosis of depression is formulated through semi-structured interviews and supplementary questionnaires, which makes the diagnosis heavily relying on physicians experience and is subject to bias. Mental health monitoring and cloud-based remote diagnosis can be implemented through an automated depression diagnosis system. In this article, we propose an attention-based multimodality speech and text representation for depression prediction. Our model is trained to estimate the depression severity of participants using the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) dataset. For the audio modality, we use the collaborative voice analysis repository (COVAREP) features provided by the dataset and employ a Bidirectional Long Short-Term Memory Network (Bi-LSTM) followed by a Time-distributed Convolutional Neural Network (T-CNN). For the text modality, we use global vectors for word representation (GloVe) to perform word embeddings and the embeddings are fed into the Bi-LSTM network. Results show that both audio and text models perform well on the depression severity estimation task, with best sequence level F1 score of 0.9870 and patient-level F1 score of 0.9074 for the audio model over five classes (healthy, mild, moderate, moderately severe, and severe), as well as sequence level F1 score of 0.9709 and patient-level F1 score of 0.9245 for the text model over five classes. Results are similar for the multimodality fused model, with the highest F1 score of 0.9580 on the patient-level depression detection task over five classes. Experiments show statistically significant improvements over previous works.