# Multimodal Depression Classification Using Articulatory Coordination Features And Hierarchical Attention Based Text Embeddings

arXiv: https://arxiv.org/abs/2202.06238 · HTML: https://mlsanity.com/paper/2202.06238/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 13 Feb 2022.

## Builds on (most cited first, 2 of 2)

- [Multi-level Attention network using text, audio and video for Depression Prediction](https://mlsanity.com/paper/1909.01417/) (2019)
- [Speech based Depression Severity Level Classification Using a Multi-Stage Dilated CNN-LSTM Model](https://mlsanity.com/paper/2104.04195/) (2021)

## Built on by (most cited first, 3 of 3)

- [Multimodal Biomarkers for Schizophrenia: Towards Individual Symptom Severity Estimation](https://mlsanity.com/paper/2505.16044/) (2025)
- [MDD-Net: Multimodal Depression Detection through Mutual Transformer](https://mlsanity.com/paper/2508.08093/) (2025)
- [Clinically Inspired Symptom-Guided Depression Detection from Emotion-Aware Speech Representations](https://mlsanity.com/paper/2602.15578/) (2026)

## Abstract (arXiv)

Multimodal depression classification has gained immense popularity over the recent years. We develop a multimodal depression classification system using articulatory coordination features extracted from vocal tract variables and text transcriptions obtained from an automatic speech recognition tool that yields improvements of area under the receiver operating characteristics curve compared to uni-modal classifiers (7.5% and 13.7% for audio and text respectively). We show that in the case of limited training data, a segment-level classifier can first be trained to then obtain a session-wise prediction without hindering the performance, using a multi-stage convolutional recurrent neural network. A text model is trained using a Hierarchical Attention Network (HAN). The multimodal system is developed by combining embeddings from the session-level audio model and the HAN text model