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Dimensionality Reduction and Attention Mechanisms for Extracting Affective State from Sound Spectrograms

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Pattern Recognition Applications and Methods (ICPRAM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12594))

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Abstract

Emotion recognition (ER) has drawn the interest of many researchers in the field of human-computer interaction, being central in such applications as assisted living and personalized content suggestion. When considering the implementation of ER capable systems, if they are to be widely adopted in daily life, one must take into account that methods for emotion recognition should work on data collected in an unobtrusive way. Out of the possible data modalities for affective state analysis, which include video and biometrics, speech is considered the least intrusive and for this reason has drawn the focus of many research efforts. In this chapter, we discuss methods for analyzing the non-linguistic component of vocalized speech for the purposes of ER. In particular, we propose a method for producing lower dimensional representations of sound spectrograms which respect their temporal structure. Moreover, we explore possible methods for analyzing such representations, including shallow methods, recurrent neural networks and attention mechanisms. Our models are evaluated on data taken from popular, public datasets for emotion analysis with promising results.

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Authors and Affiliations

  1. Department of Informatics and Telecommunications, National Kapodistrian University of Athens, Athens, Greece

    George Pikramenos & Konstantinos Kechagias

  2. National Center for Scientific Research, Demokritos, Athens, Greece

    George Pikramenos, Theodoros Psallidas, Evaggelos Spyrou & Stavros Perantonis

  3. School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

    Georgios Smyrnis

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  1. George Pikramenos
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  2. Konstantinos Kechagias
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  3. Theodoros Psallidas
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  4. Georgios Smyrnis
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  5. Evaggelos Spyrou
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  6. Stavros Perantonis
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Correspondence to George Pikramenos .

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Editors and Affiliations

  1. Sapienza Università di Roma, Roma, Italy

    Maria De Marsico

  2. ICAR, Consiglio Nazionale delle Ricerche, Naples, Napoli, Italy

    Gabriella Sanniti di Baja

  3. Instituto de Telecomunicações, Lisbon, Portugal

    Ana Fred

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Pikramenos, G., Kechagias, K., Psallidas, T., Smyrnis, G., Spyrou, E., Perantonis, S. (2020). Dimensionality Reduction and Attention Mechanisms for Extracting Affective State from Sound Spectrograms. In: De Marsico, M., Sanniti di Baja, G., Fred, A. (eds) Pattern Recognition Applications and Methods. ICPRAM 2020. Lecture Notes in Computer Science(), vol 12594. Springer, Cham. https://doi.org/10.1007/978-3-030-66125-0_3

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  • DOI: https://doi.org/10.1007/978-3-030-66125-0_3

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