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Emotion Classification from Speech by an Ensemble Strategy

Published: 25 May 2023 Publication History

Abstract

Humans are prepared to comprehend each other's emotions through subtle body movements and speech expressions, and from those, they change the way they deliver/understand messages when communicating between them. Socially assistive robots need to empower their ability in recognizing emotions in a way to change the interaction with humans, especially with elders. This paper presents a framework for speech emotion prediction supported by an ensemble of distinct out-of-the-box methods, being the main contribution of the integration of the outputs of those methods in a single prediction consistent with the expression presented by the system's user. Results show a classification accuracy of 75.56% over the RAVDESS dataset and 86.43% in a group of datasets constituted by RAVDESS, SAVEE, and TESS.

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Cited By

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  • (2023)Multimodal Emotion Classification Supported in the Aggregation of Pre-trained Classification ModelsComputational Science – ICCS 202310.1007/978-3-031-36030-5_35(433-447)Online publication date: 26-Jun-2023
  • (2023)Body-Focused Expression Analysis: A Conceptual FrameworkUniversal Access in Human-Computer Interaction10.1007/978-3-031-35897-5_42(596-608)Online publication date: 9-Jul-2023

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Information

Published In

cover image ACM Other conferences
DSAI '22: Proceedings of the 10th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion
August 2022
237 pages
ISBN:9781450398077
DOI:10.1145/3563137
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 May 2023

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Author Tags

  1. Emotions
  2. Ensembles
  3. Machine Learning
  4. Speech Emotion Recognition

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • Portuguese Foundation for Science and Technology (FCT), project LARSyS - FCT Project

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DSAI 2022

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Overall Acceptance Rate 17 of 23 submissions, 74%

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Cited By

View all
  • (2023)Multimodal Emotion Classification Supported in the Aggregation of Pre-trained Classification ModelsComputational Science – ICCS 202310.1007/978-3-031-36030-5_35(433-447)Online publication date: 26-Jun-2023
  • (2023)Body-Focused Expression Analysis: A Conceptual FrameworkUniversal Access in Human-Computer Interaction10.1007/978-3-031-35897-5_42(596-608)Online publication date: 9-Jul-2023

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