论文标题
基于RES2NET检测唤醒单词
Wake Word Detection Based on Res2Net
论文作者
论文摘要
这封信提出了一个基于RES2NET的新唤醒单词检测系统。作为RESNET的一种变体，首先将RES2NET应用于反对检测。 RES2NET通过增加可能的接受场来实现多个特征量表。这种多重缩放机制可显着提高具有不同持续时间的唤醒单词的检测能力。与基于RESNET的模型相比，RES2NET还显着降低了模型大小，并且更适合检测唤醒单词。提出的系统可以在没有任何其他帮助的情况下确定音频流中唤醒单词的位置。在包含两个唤醒单词的MOBVOI数据集上验证了所提出的方法。以每小时0.5的误报率，该系统在先前的工作中将两个唤醒单词的虚假拒绝减少了12％以上。
This letter proposes a new wake word detection system based on Res2Net. As a variant of ResNet, Res2Net was first applied to objection detection. Res2Net realizes multiple feature scales by increasing possible receptive fields. This multiple scaling mechanism significantly improves the detection ability of wake words with different durations. Compared with the ResNet-based model, Res2Net also significantly reduces the model size and is more suitable for detecting wake words. The proposed system can determine the positions of wake words from the audio stream without any additional assistance. The proposed method is verified on the Mobvoi dataset containing two wake words. At a false alarm rate of 0.5 per hour, the system reduced the false rejection of the two wake words by more than 12% over prior works.
