{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T23:03:38Z","timestamp":1768604618246,"version":"3.49.0"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"37","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.31449\/inf.v49i37.9072","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T12:02:42Z","timestamp":1768564962000},"source":"Crossref","is-referenced-by-count":0,"title":["Research on English Listening Comprehension Model Design Based on Transformer-ResNet Hybrid Model"],"prefix":"10.31449","volume":"49","author":[{"given":"Yanyan","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2025,12,24]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/9072\/6413","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/9072\/6413","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T12:02:43Z","timestamp":1768564963000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/9072"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,24]]},"references-count":0,"journal-issue":{"issue":"37","published-online":{"date-parts":[[2026,1,11]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v49i37.9072","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12,24]]}}}