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An Empirical Study on Feature Extraction in DNN-Based Speech Emotion Recognition

  • 2020
  • OriginalPaper
  • Chapter
Published in:
Authors
Panikos Heracleous
Kohichi Takai
Yanan Wang
Keiji Yasuda
Akio Yoneyama
Yasser Mohammad
Published in
HCI International 2020 – Late Breaking Posters
Publisher
Springer International Publishing

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Abstract

The current empirical study focuses on speech emotion recognition using speech data extracted from video clips. Although many studies reported speech emotion recognition, the majority of the studies presented were based on using acted and clean speech. A more challenging and realistic task would be using spontaneous noisy speech from video clips. In the current study, the modern and state-of-the-art i-vector features are applied and experimentally evaluated. Comparisons with the widely used low-level descriptors (LLDs) and functionals are also presented. To improve the classification accuracy, a method based on late fusion is investigated. Using the proposed method, higher accuracies were achieved compared to the sole use of individual features. For classification, a fully connected deep neural network (DNN) with several hidden layers was used.

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Title
An Empirical Study on Feature Extraction in DNN-Based Speech Emotion Recognition
Authors
Panikos Heracleous
Kohichi Takai
Yanan Wang
Keiji Yasuda
Akio Yoneyama
Yasser Mohammad
Copyright Year
2020
Book
HCI International 2020 – Late Breaking Posters

Print ISBN: 978-3-030-60699-2

Electronic ISBN: 978-3-030-60700-5

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-60700-5_40
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