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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Heracleous, P., Takai, K., Wang, Y., Yasuda, K., Yoneyama, A., Mohammad, Y. (2020). An Empirical Study on Feature Extraction in DNN-Based Speech Emotion Recognition. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2020 – Late Breaking Posters. HCII 2020. Communications in Computer and Information Science, vol 1293. Springer, Cham. https://doi.org/10.1007/978-3-030-60700-5_40
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DOI: https://doi.org/10.1007/978-3-030-60700-5_40
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