Advancing English Language Learning with Biotechnology: Utilizing Domain Adaptive Convolutional Neural Networks

Abstract

In this paper, we explore a neural network-based approach for English language education, integrating advancements in biotechnology to enhance an AI-driven instructional model. We delve into various data analysis indicators pertinent to this biotechnological integration. These indicators encompass various human-computer interaction behaviors such as learning, testing, participation, and resource search activities. Moreover, we consider demographic background, learning capabilities, and attitudes—key characteristics influenced by biotechnological factors—that impact the learning outcome. Significant efforts have been made to amass these indicators, incorporating biotechnological insights for a comprehensive understanding. We propose an audiovisual integration method centred around a Convolutional Neural Network (CNN). This method leverages independent CNN architectures for distinct modeling of audiovisual perception and asynchronous data transmission. It captures the representation of audiovisual parallel data in a high-dimensional feature space. The subsequent layers, adhering to a common fully connected structure, enable modeling of the extended dependency of audiovisual data in higher dimensions, benefiting from biotechnological insights. Our experiments demonstrate that the Automatic Speech Recognition (AVSR) system, developed using this CNN-based audiovisual integration method, shows a marked performance enhancement. The error rate is reduced by approximately 15% compared to traditional models. Furthermore, the speech recognition system, trained with a cross-domain adaptive approach that incorporates biotechnological methodologies, achieves a significant performance boost. Its error rate is over 10% lower than that of the conventional benchmark system, showcasing the effective synergy of biotechnology and neural network models in enhancing English language learning.