With the acceleration of globalization, the demand for intelligent cross-cultural English communication is increasing day by day. This paper proposes a cross-cultural English communication intelligent model system integrating multi-scale wavelet analysis, aiming to improve the cultural sensitivity and context understanding ability of intelligent systems through multi-modal data fusion, multi-scale feature extraction, and cross-cultural adaptive modeling. Innovations include: (1) Combining the multi-scale analysis ability of wavelet transform with deep learning to optimize the time-frequency feature extraction of language signals; (2) This system deeply integrates the multi-scale analysis ability of wavelet transform with deep learning models (such as CNN-LSTM), and realizes integrated modeling of cross-cultural semantic understanding and adaptive generation through time-frequency localization feature extraction of speech signals and dynamic alignment of text and visual modalities. (3) Construct a cultural adaptability evaluation module based on the cross-cultural sensitivity development model (DMIS), and dynamically adjust the system output to adapt to the communication needs of users with different cultural backgrounds; (4) Design a multi-modal data fusion framework to integrate speech, text and non-verbal behavior features to enhance the robustness of semantic parsing in cross-cultural scenarios. Experimental results demonstrate that the system achieves a semantic understanding accuracy of 90.9% in cross-cultural contexts, a 12.7% improvement over the baseline model; its Culture Adaptability Index reaches 4.2 out of 5, outperforming advanced models such as GPT-4. Furthermore, in multimodal conflict scenarios, the false alarm rate is reduced to 9.3%, and the response latency is only 280ms. This study provides a new theoretical framework and technical path for a cross-cultural communication intelligent system.
Wang et al. (Sun,) studied this question.