# Sentiment recognition of Italian elderly through domain adaptation on cross-corpus speech dataset

arXiv: https://arxiv.org/abs/2211.07307 · HTML: https://mlsanity.com/paper/2211.07307/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 62 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages](https://mlsanity.com/paper/1812.10411/) (2018)
- [LSSED: a large-scale dataset and benchmark for speech emotion recognition](https://mlsanity.com/paper/2102.01754/) (2021)
- [Deep neural networks for emotion recognition combining audio and transcripts](https://mlsanity.com/paper/1911.00432/) (2019)
- [Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning](https://mlsanity.com/paper/1909.02764/) (2019)
- [Is Everything Fine, Grandma? Acoustic and Linguistic Modeling for Robust Elderly Speech Emotion Recognition](https://mlsanity.com/paper/2009.03432/) (2020)

## Built on by (most cited first, 1 of 1)

- [SER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adults](https://mlsanity.com/paper/2311.14483/) (2023)

## Abstract (arXiv)

The aim of this work is to define a speech emotion recognition (SER) model able to recognize positive, neutral and negative emotions in natural conversations of Italian elderly people. Several datasets for SER are available in the literature. However most of them are in English or Chinese, have been recorded while actors and actresses pronounce short phrases and thus are not related to natural conversation. Moreover only few speeches among all the databases are related to elderly people. Therefore, in this work, a multi-language and multi-age corpus is considered merging a dataset in English, that includes also elderly people, with a dataset in Italian. A general model, trained on young and adult English actors and actresses is proposed, based on XGBoost. Then two strategies of domain adaptation are proposed to adapt the model either to elderly people and to Italian speakers. The results suggest that this approach increases the classification performance, underlining also that new datasets should be collected.