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Survey of Transfer Learning and a Case Study of Emotion Recognition Using Inductive Approach

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Data Management, Analytics and Innovation

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1175))

Abstract

In the era of rapid processing, there is a need for application developments that work with different datasets. The novel learning algorithms designed to handle different training and testing sets. Transfer learning allows domains, tasks, and distributions used in training and testing to be different. The concept of Transfer Learning [TL] is motivated by the detail that we can smear knowledge learned formerly to resolve new complications with ease, reusing the knowledge domain. In this paper, we would like to present inductive TL mechanisms to predict emotions from two different languages English and German, where knowledge of emotion identification exists in English language and is extended to learn emotions in German speech. We have attempted to uncover latent features of one language in another language.

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Authors and Affiliations

  1. PESU, Bangalore, India

    Abhinand Poosarala & R Jayashree

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  1. Abhinand Poosarala
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  2. R Jayashree
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Correspondence to Abhinand Poosarala .

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Editors and Affiliations

  1. Society for Data Science, Pune, Maharashtra, India

    Neha Sharma

  2. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, West Bengal, India

    Amlan Chakrabarti

  3. Department of Automatics and Applied Software, Faculty of Engineering, University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. IT4Innovations, VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Jan Martinovic

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Poosarala, A., Jayashree, R. (2021). Survey of Transfer Learning and a Case Study of Emotion Recognition Using Inductive Approach. In: Sharma, N., Chakrabarti, A., Balas, V.E., Martinovic, J. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1175. Springer, Singapore. https://doi.org/10.1007/978-981-15-5619-7_9

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