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Feature Analysis for Emotional Content Comparison in Speech

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Advances in Computational Intelligence Systems (UKCI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1043))

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Abstract

Emotional content analysis is getting more and more present in speech-based human machine interaction, such as emotion recognition and expressive speech synthesis. In this framework, this paper aims to compare the emotional content of a pair of speech signals, uttered by different speakers and not necessarily having the same text. This exploratory work employs machine learning methods to analyze emotional content in speech from different angles: (a) Evaluate the relevance of the used features in the analysis of emotions, (b) Calculate the similarity of the emotional content independently from speakers and text. The final goal is to provide a metric to compare emotional content in speech. Such a metric would form the basis for higher-level tasks, such as clustering utterances by emotional content, or applying kernel methods for expressive speech analysis.

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Acknowlegments

This work was supported by the research grant “Fondi di ricerca di ateneo 2016” of the university of Genova.

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Authors and Affiliations

  1. DIBRIS, University of Genova, Genoa, Italy

    Zied Mnasri, Stefano Rovetta & Francesco Masulli

  2. Electrical Engineering Department, ENIT, University Tunis El Manar, Tunis, Tunisia

    Zied Mnasri

  3. Sbarro Institute for Cancer Research and Molecular Medicine, Temple University, Philadelphia, USA

    Francesco Masulli

Authors
  1. Zied Mnasri
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  2. Stefano Rovetta
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  3. Francesco Masulli
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Corresponding author

Correspondence to Zied Mnasri .

Editor information

Editors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, UK

    Zhaojie Ju

  2. Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK

    Longzhi Yang

  3. Bristol Robotics Laboratory, University of the West of England, Bristol, UK

    Chenguang Yang

  4. School of Computing, University of Portsmouth, Portsmouth, Hampshire, UK

    Alexander Gegov

  5. School of Computing, University of Portsmouth, Portsmouth, UK

    Dalin Zhou

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Mnasri, Z., Rovetta, S., Masulli, F. (2020). Feature Analysis for Emotional Content Comparison in Speech. In: Ju, Z., Yang, L., Yang, C., Gegov, A., Zhou, D. (eds) Advances in Computational Intelligence Systems. UKCI 2019. Advances in Intelligent Systems and Computing, vol 1043. Springer, Cham. https://doi.org/10.1007/978-3-030-29933-0_41

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