论文标题
个性化关键字通过多任务学习
Personalized Keyword Spotting through Multi-task Learning
论文作者
论文摘要
关键字斑点（KWS）在启用智能设备上的基于语音的用户互动方面起着至关重要的作用，而常规KWS（C-KWS）方法集中在检测用户无关的预定关键字上。但是，实际上，大多数用户互动都来自该设备中注册的目标用户，这些用户促使构建个性化关键字斑点。我们设计了两个个性化的KWS任务； （1）目标用户有偏见的KWS（TB-KWS）和（2）仅目标用户KWS（to-KWS）。为了解决任务，我们通过多任务学习（PK-MTL）提出了个性化关键字，该关键字可以通过多任务学习和任务适应组成。首先，我们介绍对关键字发现和扬声器验证的多任务学习，以利用用户信息到关键字发现系统。接下来，我们设计特定于任务的评分功能，以彻底适应个性化的KWS任务。我们在常规和个性化场景上评估了框架，结果表明，PK-MTL可以大大降低错误警报率，尤其是在各种实际情况下。
Keyword spotting (KWS) plays an essential role in enabling speech-based user interaction on smart devices, and conventional KWS (C-KWS) approaches have concentrated on detecting user-agnostic pre-defined keywords. However, in practice, most user interactions come from target users enrolled in the device which motivates to construct personalized keyword spotting. We design two personalized KWS tasks; (1) Target user Biased KWS (TB-KWS) and (2) Target user Only KWS (TO-KWS). To solve the tasks, we propose personalized keyword spotting through multi-task learning (PK-MTL) that consists of multi-task learning and task-adaptation. First, we introduce applying multi-task learning on keyword spotting and speaker verification to leverage user information to the keyword spotting system. Next, we design task-specific scoring functions to adapt to the personalized KWS tasks thoroughly. We evaluate our framework on conventional and personalized scenarios, and the results show that PK-MTL can dramatically reduce the false alarm rate, especially in various practical scenarios.
