Citation

Tian, Hong and Gui, Hailan and Wu, Xuanyi (2026) Investigation and Implementation of Japanese Speech Translation Recognition System Based on Long Short-Term Memory Neural Network. International Journal of Information Technologies and Systems Approach, 19 (1). pp. 1-20. ISSN 1935-570X

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Abstract

This study constructs a model suitable for studying the performance of a Japanese speech recognition translation system based on a long short-term memory (LSTM) neural network. The impact of the matrix on the accuracy and recognition ratio of the speech recognition system is considered. This study provides a general introduction to the LSTM neural network, gives an overview of a Japanese speech recognition translation system, and presents experimental data to evaluate its performance. Then, the LSTM neural network is recommended for studying the Japanese speech translation recognition system, and a suitable model is established to conduct experiments on the system. By comparing example analyses, the experimental results show that the optimized PLSTM (Part-Aware Long Short-Term Memory) achieved an 8.09% improvement over an unoptimized LSTM network while maintaining recognition accuracy.

Item Type:	Article
Uncontrolled Keywords:	LSTM Neural Network, Recurrent Neural Network
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Oct 2026 08:08
Last Modified:	30 Oct 2026 08:08
URII:	http://shdl.mmu.edu.my/id/eprint/17065

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