Target speech extraction (TSE) typically relies on pre-recorded high-quality enrollment speech, which disrupts user experience and limits feasibility in spontaneous interaction. In this paper, we propose Enroll-on-Wakeup (EoW), a novel framework where the wake-word segment, captured naturally during human-machine interaction, is automatically utilized as the enrollment reference. This eliminates the need for pre-collected speech to enable a seamless experience. We perform the first systematic study of EoW-TSE, evaluating advanced discriminative and generative models under real diverse acoustic conditions. Given the short and noisy nature of wake-word segments, we investigate enrollment augmentation using LLM-based TTS. Results show that while current TSE models face performance degradation in EoW-TSE, TTS-based assistance significantly enhances the listening experience, though gaps remain in speech recognition accuracy.

翻译：目标语音提取（TSE）通常依赖预录制的高质量注册语音，这会破坏用户体验并限制其在自发交互中的可行性。本文提出“基于唤醒注册”（EoW）这一新颖框架，其中人机交互过程中自然捕获的唤醒词片段被自动用作注册参考。这消除了对预采集语音的需求，实现了无缝体验。我们首次对EoW-TSE进行系统性研究，评估了在真实多样声学条件下先进的判别式与生成式模型。鉴于唤醒词片段具有短时与嘈杂特性，我们利用基于大语言模型的文本转语音（LLM-based TTS）技术研究了注册增强方法。结果表明，尽管当前TSE模型在EoW-TSE场景中性能有所下降，但基于TTS的辅助显著提升了听觉体验，不过在语音识别准确性方面仍存在差距。