IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i8p1879-d1124385.html
   My bibliography  Save this article

A Mandarin Tone Recognition Algorithm Based on Random Forest and Feature Fusion †

Author

Listed:
  • Jiameng Yan

    (School of Microelectronics, Shandong University, Jinan 250100, China
    These authors contributed equally to this work and are co-first authors.)

  • Qiang Meng

    (School of Microelectronics, Shandong University, Jinan 250100, China
    These authors contributed equally to this work and are co-first authors.)

  • Lan Tian

    (School of Microelectronics, Shandong University, Jinan 250100, China)

  • Xiaoyu Wang

    (School of Microelectronics, Shandong University, Jinan 250100, China)

  • Junhui Liu

    (School of Microelectronics, Shandong University, Jinan 250100, China)

  • Meng Li

    (China Telecom Shandong Branch, Jinan 250098, China)

  • Ming Zeng

    (School of Microelectronics, Shandong University, Jinan 250100, China)

  • Huifang Xu

    (School of Microelectronics, Shandong University, Jinan 250100, China)

Abstract

In human–computer interaction (HCI) systems for Mandarin learning, tone recognition is of great importance. A brand-new tone recognition method based on random forest (RF) and feature fusion is proposed in this study. Firstly, three fusion feature sets (FFSs) were created by using different fusion methods on sound source features linked to Mandarin syllable tone. Following the construction of the CART decision trees using the three FFSs, modeling and optimization of the corresponding RF tone classifiers were performed. The method was tested and evaluated on the Syllable Corpus of Standard Chinese (SCSC), which is a speaker-independent Mandarin monosyllable corpus. Additionally, the effects were also assessed on small sample sets. The results show that the tone recognition algorithm can achieve high tone recognition accuracy and has good generalization capability and classification ability with unbalanced data. This indicates that the proposed approach is highly efficient and robust and is appropriate for mobile HCI learning systems.

Suggested Citation

  • Jiameng Yan & Qiang Meng & Lan Tian & Xiaoyu Wang & Junhui Liu & Meng Li & Ming Zeng & Huifang Xu, 2023. "A Mandarin Tone Recognition Algorithm Based on Random Forest and Feature Fusion †," Mathematics, MDPI, vol. 11(8), pages 1-15, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1879-:d:1124385
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/8/1879/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/8/1879/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1879-:d:1124385. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.