Volume 19, Issue 4 (December 2023)                   IJEEE 2023, 19(4): 2542-2542 | Back to browse issues page


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Hasheminejad M. A Novel Nonparametric Kernel for Speech Emotion Recognition. IJEEE 2023; 19 (4) :2542-2542
URL: http://ijeee.iust.ac.ir/article-1-2542-en.html
Abstract:   (379 Views)
The Nonparametric Speech Kernel (NSK), a nonparametric kernel technique, is presented in this study as a novel way to improve Speech Emotion Recognition (SER). The method aims to effectively reduce the size of speech features to improve recognition accuracy. The proposed approach addresses the need for efficient and compact low-dimensional features for speech emotion recognition. Having acknowledged the intrinsic distinctions between speech and picture data, we have refined the Kernel Nonparametric Weighted Feature Extraction (KNWFE) formulation to suggest NSK, which is especially intended for speech emotion identification. The output of NSK can be used as input features for deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), or hybrid architectures. In deep learning, NSK can also be used as a kernel function for kernel-based methods such as kernelized support vector machines (SVM) or kernelized neural networks. Our tests demonstrate that NSK outperforms current techniques, outperforming the best-tested approach by 5.02% and 3.05%, respectively, with an average accuracy of 96.568% for the Persian speech emotion dataset and 82.56% for the Berlin speech emotion dataset.
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Type of Study: Research Paper | Subject: Speech Processing
Received: 2022/05/30 | Revised: 2024/01/04 | Accepted: 2023/12/13

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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.