Title: Smart product's human-computer interaction voice recognition method based on a novel feedforward network learning algorithm

Authors: Lian Xue; Chengsong Hu

Addresses: College of Artificial Intelligence, Wuhan Technology and Business University, Wuhan 430056, China ' College of Computer Science and Automation Engineering, Wuhan Technology and Business University, Wuhan 430056, China

Abstract: To avoid the impact of noise factors in speech recognition and improve the effectiveness of speech recognition, this paper proposes an intelligent product human-computer interaction speech recognition method based on a novel feedforward network learning algorithm. According to the dependent variable transformation relationship of nonlinear functions, a feedforward neural network speech recognition model is constructed. This model utilises the dependent variable transformation mechanism of nonlinear functions to more flexibly simulate the complex mapping relationship between speech signals and recognition results. A semi-supervised loss function is introduced into the model training, and stochastic gradient descent is used for iterative optimisation to achieve human-computer interaction speech recognition. Experiments have proven that the speech recognition accuracy of the method in this paper remains above 90%, and the speech recognition delay remains below 1 second, indicating good recognition performance, reliability, and application performance.

Keywords: feedforward network learning algorithm; intelligent product human-computer interaction; speech recognition; network model.

DOI: 10.1504/IJPD.2025.149685

International Journal of Product Development, 2025 Vol.29 No.3/4, pp.247 - 260

Received: 20 Aug 2024
Accepted: 29 May 2025
Published online: 10 Nov 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article