Title: Deep learning-powered automatic assessment mechanism in enhancing spoken English fluency

Authors: Chunyan Xu

Addresses: Huanghe Science and Technology University, Zhengzhou, China

Abstract: Spoken English is essential for individuals who wish to work or study in an English-speaking environment. It is the primary means of communication for many professions, including business, education and healthcare. To improve the efficiency of spoken English learning, an end-to-end automatic English assessment method based on deep learning is designed. At the input level, the words are represented as a sequence tensor, where each position corresponds to the pre-trained word vector and the high-level information is obtained using a bi-directional Long Short-Term Memory (LSTM) network. The attention mechanism is integrated into the network in the acoustic model layer to improve the method's efficiency. In the output layer, the expression of words is connected with the spoken English expression, and the Softmax function is used to predict the grades. Simulation results show that the proposed method performs better than traditional LSTM and gate recurrent unit.

Keywords: spoken English; automatic assessment; deep learning; LSTM.

DOI: 10.1504/IJCAT.2025.150332

International Journal of Computer Applications in Technology, 2025 Vol.77 No.3/4, pp.255 - 262

Received: 11 Jul 2024
Accepted: 31 Jul 2025
Published online: 09 Dec 2025 *

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