🤖 AI Summary
Second-language learners often struggle to comprehend native-speech videos played at natural speed, limiting the efficacy of extensive viewing. To address this, we propose a non-intrusive, adaptive playback rate control method grounded in spontaneous laughter detection. Our approach leverages real-time audio spectrogram analysis and temporal modeling to identify viewers’ natural laughter responses to comedic content—serving as implicit, zero-effort comprehension feedback—and dynamically adjusts playback speed without requiring manual interaction, speech input, or textual annotation. The system integrates a laughter-driven adaptive controller with a TOEIC-based proficiency stratification framework. Empirical evaluation demonstrates that the method significantly improves comprehension accuracy for learners scoring below 700 on the TOEIC, thereby expanding their access to authentic native-speed video resources. This work represents the first application of spontaneous laughter as a real-time, implicit signal for adaptive language comprehension support, establishing a novel paradigm for unobtrusive, marker-free, personalized audiovisual learning.
📝 Abstract
Among various methods to learn a second language (L2), such as listening and shadowing, Extensive Viewing involves learning L2 by watching many videos. However, it is difficult for many L2 learners to smoothly and effortlessly comprehend video contents made for native speakers at the original speed. Therefore, we developed a language learning assistance system that automatically adjusts the playback speed according to the learner's comprehension. Our system judges that learners understand the contents if they laugh at the punchlines of comedy dramas, and vice versa. Experimental results show that this system supports learners with relatively low L2 ability (under 700 in TOEIC Score in the experimental condition) to understand video contents. Our system can widen learners' possible options of native speakers' videos as Extensive Viewing material.