Title: Chinese speech emotion recognition based on improved convolutional neural network

Authors: Xiaoyan Wei; Xinhua Wang

Addresses: Editorial Department of Journal of Shangqiu Polytechnic, Shangqiu, 476100, China ' Henan Police College, Zhengzhou, 450046, China

Abstract: A Chinese speech emotion recognition method based on improved convolutional neural network is proposed with the expected goal of solving the problems of high false acceptance rate and false rejection rate and high recognition time consumption in traditional Chinese speech emotion recognition methods. Collect Chinese speech signals and perform pre emphasis, framing, windowing, and fast Fourier transform on the collected signals to achieve pre-processing of Chinese speech signals and extract features of the pre-processed Chinese speech signals. Introducing multi-level residuals to improve the convolutional neural network, inputting Chinese speech signal features into the improved convolutional neural network, and iteratively outputting Chinese speech emotion recognition results. Through experimental testing, it has been proven that the proposed method has an average false acceptance rate of 2.84% and an average false rejection rate of 4.63%. The maximum time consumption for Chinese speech emotion recognition is 49.2 ms.

Keywords: improved convolutional neural network; Chinese speech; emotion recognition; fast Fourier transform; FFT; multi-level residuals.

DOI: 10.1504/IJBM.2026.151098

International Journal of Biometrics, 2026 Vol.18 No.1/2/3, pp.230 - 246

Received: 18 Feb 2025
Accepted: 10 May 2025
Published online: 13 Jan 2026 *

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