为韩语设计端到端唤醒词系统并集成语音认证，提升隐私安全性。
An End-to-End Approach for Korean Wakeword Systems with Speaker Authentication
- 用全连接网络实现韩语唤醒词检测，支持端到端训练。
- 唤醒词检测与语音认证分别达到16.79%和6.6%的等错误率。
- 适合关注多语言语音隐私保护的开发者与研究者。
唤醒词检测在使人工智能助手响应用户语音、实现有效交互中至关重要。然而，除英语外，其他语言缺乏预训练唤醒词模型。仅判断唤醒词是否存在会带来严重隐私风险。本文提出一种端到端方法，针对非英语语言（特别是韩语）训练唤醒词，并构建语音认证模型以保护用户隐私。系统基于开源平台OpenWakeWord，采用全连接网络（FCN）进行唤醒词检测。一旦检测到唤醒词，自研代码通过余弦相似度计算实现稳健的用户认证。实验结果表明，该方法在唤醒词检测和语音认证任务中分别取得16.79%和6.6%的等错误率（EER），验证了其在韩语用户中提供安全、准确唤醒与认证的潜力。
原文摘要 · Abstract (English)
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.
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