Understanding how media rhetoric shapes audience engagement is crucial in the attention economy. This study examines how moral-emotional framing by mainstream news channels on YouTube influences user behavior across Korea and the United States. To capture the platform's multimodal nature, combining thumbnail images and video titles, we develop a multimodal moral emotion classifier by fine-tuning a vision–language model. The model is trained on human-annotated multimodal datasets in both languages and applied to approximately 400,000 videos from major news outlets. We analyze three engagement levels (views, likes, and comments), representing increasing degrees of commitment. The results show that other-condemning rhetoric—expressions of moral outrage that criticize others' morality—consistently increases all forms of engagement across cultures, with effect sizes strengthening from passive viewing to active commenting. These findings suggest that moral outrage is a particularly effective emotional strategy, attracting not only attention but also active participation. We discuss concerns about the potential misuse of other-condemning rhetoric, as such practices may deepen polarization by reinforcing in-group/out-group divisions. To facilitate future research and ensure reproducibility, we publicly release our Korean and English multimodal moral emotion classifiers.

Several recent target speaker extraction (TSE) models directly utilize enrollment speech without explicitly extracting low-dimensional speaker embeddings. However, these methods typically inject the speaker information only once at the input of the speaker extraction network, which may be insufficient because the conditioning information can become diluted as it propagates through repeated separator blocks. In this letter, we propose a TSE model built upon the TF-GridNet, which is a speech separation model performing dual-path modeling in the time-frequency domain with cross-frame self-attention modules. In the proposed TSE model, the self-attention modules in the first$M$separator blocks are replaced by cross-attention between the enrollment speech and the mixture signal, providing speaker information in multiple stages without introducing additional parameters or computation compared with the original TF-GridNet blocks. In addition, the initial hidden and cell states of the inter-frame long short-term memory (LSTM) modules are determined for each frequency from the enrollment speech. As the pattern of the temporal correlation may be different for each frequency depending on the pitch and speaking style, speaker-dependent frequency-wise state initialization would be helpful. Experimental results showed that the proposed TSE model demonstrated the best PESQ scores and comparable SI-SDRs with lower computational complexity.

Many monaural speech separation models assume that the exact number of speakers is known in advance, which is not applicable to many real-world scenarios. To deal with an unknown number of speakers, previous approaches either iteratively separate one speech at a time, or employ a more relaxed assumption that the maximum number of speakers is known a priori and set the number of outputs accordingly. When the number of speakers in the mixture is smaller than the number of outputs in the latter case, the extra outputs that are not mapped onto signals in the input mixture are trained to produce predefined target signals such as the silence or the input mixture. In this letter, we propose to ignore the extra outputs in training instead of evaluating the cost with a certain target for separation models with a fixed number of output channels. We also introduce a method to select valid output signals. Experimental results showed that assigning any type of predefined targets degraded separation performance compared with ignoring the extra outputs.