arXiv:2607. 22794v1 Announce Type: cross Abstract: Automatic depression detection with deep learning has shown promise but often suffers from limited generalization due to domain shift arising from inter-speaker variability.
By Ali Tabaraei, Federico Simonetta, Stavros Ntalampiras
arXiv:2605.22286v2 Announce Type: replace-cross
Abstract: Text-based counseling provides a valuable source of information for assessing depression severity. We study prediction of the total score on...
By Zhaomin Wu, Jiayi Li, Bingsheng He
DiaRelay introduces a lightweight adapter that lets large language models maintain a constant‑size dialogue‑level memory for emotion recognition in conversation. It builds on LoRA by adding a Selective Relay Memory Transition that aggregates useful historical evidence into a bounded memory, and a Dual‑axis Relay Memory Read that uses this memory to modulate low‑rank feature transformations. Experiments show DiaRelay achieves state‑of‑the‑art weighted F1 and accuracy on MELD with only 7.1 M additional trainable parameters, while also performing competitively on IEMOCAP.
By Zihao Zhou, Bin Yang, Jinghui Qin, Kebing Jin
The paper presents a bi‑modal speech‑level transformer that eliminates segment‑level labeling and introduces a hierarchical attention interpretation method. By using gradient‑weighted attention maps from all attention layers, the model provides both speech‑level and sentence‑level explanations of depression detection. Experimental results show the transformer outperforms a segment‑level model (p=0.854 vs. 0.732, r=0.947 vs. 0.808, F1=0.897 vs. 0.768).
By Qingkun Deng, Saturnino Luz, Sofia de la Fuente Garcia
arXiv:2606. 10278v1 Announce Type: cross Abstract: Speech Emotion Recognition (SER) aims to identify a speaker's emotional state from audio signals.
By Youcef Soufiane Gheffari, Samiya Silarbi
arXiv:2606. 25606v1 Announce Type: cross Abstract: Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for early diagnosis and intervention.
By Felipe Moreno, Sharifa Alghowinem, Hae Won Park, Cynthia Breazeal