Research on English Listening Comprehension Model Design Based on Transformer-ResNet Hybrid Model - Wang, Yanyan (Informatica, 2025)
With the increasing complexity of English listening comprehension tasks, the traditional single acoustic model has made it difficult to cope with the high noise interference and multi-level semantic understanding requirements in complex speech environments. Based on the research on the design of the English listening comprehension model based on the Transformer-ResNet hybrid model, an innovative architecture combining residual convolutional network and self-attention mechanism is proposed, aiming to improve the model's performance in long-term dependency modeling and local acoustic pattern recognition. A parallel dual-stream feature extraction architecture is designed, using ResNet to extract fine-grained acoustic features and the Transformer self-attention mechanism to capture long-term semantic dependencies. In order to solve the alignment problem between phoneme-level and semantic-level features, a cross-layer connection strategy is proposed, and the robustness of the model is improved by multi-scale feature fusion. Due to the limitation of real-time and computing resources, model compression and distillation technology are adopted to optimize computing efficiency, and an efficient end-to-end speech understanding system is realized by combining the pre-trained language model. The optimized hybrid model performed outstandingly on the test set, with an overall accuracy rate of 78.9%, an increase of 10.1 percentage points compared with the baseline model. The Transformer module for long-term dependence modeling contributed a 32% performance gain. At the same time, ResNet's local feature extraction capability enabled the model to maintain a time series consistency score of 66.6 in 44 sets of consecutive speech frame processing. It is worth noting that the model can still maintain a low word error rate of 9.8 in 87% of multi-speaker scenarios, indicating its robustness advantage in complex auditory environments. The experimental data verify the effectiveness of the complementary design of Transformer and ResNet in improving English listening comprehension tasks.