SER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adults

Alessandra Grossi, Francesca Gasparini
11/24/2023
eess.AScs.CLcs.SD

No confident code implementation found for this paper.

Abstract

In this paper, SER_AMPEL, a multi-source dataset for speech emotion recognition (SER) is presented. The peculiarity of the dataset is that it is collected with the aim of providing a reference for speech emotion recognition in case of Italian older adults. The dataset is collected following different protocols, in particular considering acted conversations, extracted from movies and TV series, and recording natural conversations where the emotions are elicited by proper questions. The evidence of the need for such a dataset emerges from the analysis of the state of the art. Preliminary considerations on the critical issues of SER are reported analyzing the classification results on a subset of the proposed dataset.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition

87% match1 repo212023

A cross-corpus study on speech emotion recognition

86% match2022

BANSpEmo: A Bangla Emotional Speech Recognition Dataset

85% match2023

ShEMO -- A Large-Scale Validated Database for Persian Speech Emotion Detection

85% match2 repos392019

Exploring Emotion Expression Recognition in Older Adults Interacting with a Virtual Coach

84% match2023

Experimenting with Affective Computing Models in Video Interviews with Spanish-speaking Older Adults

84% match2025

Cite this paper

@article{grossi2023serampel,
  title  = {SER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adults},
  author = {Alessandra Grossi and Francesca Gasparini},
  year   = {2023},
  eprint = {2311.14483},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.14483v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2311.14483.svg)](https://codeofpaper.com/paper/2311.14483)

Discussion