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

Jiameng Yan, Qiang Meng, Lan Tian (), Xiaoyu Wang, Junhui Liu, Meng Li, Ming Zeng and Huifang Xu
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Jiameng Yan: School of Microelectronics, Shandong University, Jinan 250100, China
Qiang Meng: School of Microelectronics, Shandong University, Jinan 250100, China
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

Mathematics, 2023, vol. 11, issue 8, 1-15

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.

Keywords: tone recognition; random forest; feature fusion; Mandarin (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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