In this letter, we propose a neural zero-shot voice conversion (ZS-VC) system that simultaneously achieves high speaker similarity and speech intelligibility by incorporating a content-aware style generation module. Although recent neural ZS-VC systems have shown strong performance in either speaker similarity or speech intelligibility, attaining high performance in both remains challenging, especially when only a short target speech sample is available. We attribute this limitation to the insufficient content problem—where the linguistic content of the target speech fails to fully cover that of the source speech. To address this issue, we introduce a method that augments the target speaker's style features for underrepresented content using self-supervised feature generation. Experimental results demonstrate that the proposed system, when integrated with the feature matching-based approach kNN-VC, outperforms existing methods in both key metrics. Demo samples are available at https://hyeonjincha.github.io/.

Conversational agents have traditionally been developed for either task-oriented dialogue (TOD) or open-ended chitchat, with limited progress in unifying the two.Yet, real-world conversations naturally involve fluid transitions between these modes.To address this gap, we introduce TACT (TOD-And-Chitchat Transition), a dataset designed for transitionaware dialogue modeling that incorporates structurally diverse and integrated mode flows.TACT supports both user-and agent-driven mode switches, enabling robust modeling of complex conversational dynamics.To evaluate an agent's ability to initiate and recover from mode transitions, we propose two new metrics-Switch and Recovery.Models trained on TACT outperform baselines in both intent detection and mode transition handling.Moreover, applying Direct Preference Optimization (DPO) to TACT-trained models yields additional gains, achieving 75.74% joint mode-intent accuracy and a 70.1% win rate against GPT-4O in human evaluation.These results demonstrate that pairing structurally diverse data with DPO enhances response quality and transition control, paving the way for more proactive and transition-aware conversational agents.