Wakeword detection plays a critical role in enabling AI assistants to listen to user voices and interact effectively. However, for languages other than English, there is a significant lack of pre-trained wakeword models. Additionally, systems that merely determine the presence of a wakeword can pose serious privacy concerns. In this paper, we propose an end-to-end approach that trains wakewords for Non-English languages, particulary Korean, and uses this to develop a Voice Authentication model to protect user privacy. Our implementation employs an open-source platform OpenWakeWord, which performs wakeword detection using an FCN (Fully-Connected Network) architecture. Once a wakeword is detected, our custom-developed code calculates cosine similarity for robust user authentication. Experimental results demonstrate the effectiveness of our approach, achieving a 16.79% and a 6.6% Equal Error Rate (EER) each in the Wakeword Detection and the Voice Authentication. These findings highlight the model's potential in providing secure and accurate wakeword detection and authentication for Korean users.

翻译：唤醒词检测在使AI助手能够聆听用户语音并有效交互方面发挥着关键作用。然而，对于英语以外的语言，预训练的唤醒词模型严重缺乏。此外，仅能判断唤醒词是否存在的系统可能引发严重的隐私问题。本文提出一种端到端方法，用于训练非英语语言（特别是韩语）的唤醒词，并利用此开发一种语音认证模型以保护用户隐私。我们的实现采用开源平台OpenWakeWord，该平台使用FCN（全连接网络）架构执行唤醒词检测。一旦检测到唤醒词，我们自主开发的代码会计算余弦相似度以实现鲁棒的用户认证。实验结果表明了该方法的有效性，在唤醒词检测和语音认证中分别实现了16.79%和6.6%的等错误率（EER）。这些发现凸显了该模型在为韩语用户提供安全、准确的唤醒词检测与认证方面的潜力。