# Is Everything Fine, Grandma? Acoustic and Linguistic Modeling for Robust Elderly Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2009.03432 · HTML: https://mlsanity.com/paper/2009.03432/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [A Persian ASR-based SER: Modification of Sharif Emotional Speech Database and Investigation of Persian Text Corpora](https://mlsanity.com/paper/2211.09956/) (2022)
- [Sentiment recognition of Italian elderly through domain adaptation on cross-corpus speech dataset](https://mlsanity.com/paper/2211.07307/) (2022)
- [Acoustic-Linguistic Features for Modeling Neurological Task Score in Alzheimer's](https://mlsanity.com/paper/2209.06085/) (2022)
- [SER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adults](https://mlsanity.com/paper/2311.14483/) (2023)
- [PARROT: Synergizing Mamba and Attention-based SSL Pre-Trained Models via Parallel Branch Hadamard Optimal Transport for Speech Emotion Recognition](https://mlsanity.com/paper/2506.01138/) (2025)

## Abstract (arXiv)

Acoustic and linguistic analysis for elderly emotion recognition is an under-studied and challenging research direction, but essential for the creation of digital assistants for the elderly, as well as unobtrusive telemonitoring of elderly in their residences for mental healthcare purposes. This paper presents our contribution to the INTERSPEECH 2020 Computational Paralinguistics Challenge (ComParE) - Elderly Emotion Sub-Challenge, which is comprised of two ternary classification tasks for arousal and valence recognition. We propose a bi-modal framework, where these tasks are modeled using state-of-the-art acoustic and linguistic features, respectively. In this study, we demonstrate that exploiting task-specific dictionaries and resources can boost the performance of linguistic models, when the amount of labeled data is small. Observing a high mismatch between development and test set performances of various models, we also propose alternative training and decision fusion strategies to better estimate and improve the generalization performance.