The development of deep learning technology has greatly promoted the
performance improvement of automatic speech recognition (ASR) technology, which
has demonstrated an ability comparable to human hearing in many tasks. Voice
interfaces are becoming more and more widely used as input for many
applications and smart devices. However, existing research has shown that DNN
is easily disturbed by slight disturbances and makes false recognition, which
is extremely dangerous for intelligent voice applications controlled by voice.

深度学习技术发展促进了语音识别技术的性能提高，但是现有研究表明 DNN 对微小干扰敏感且容易产生误识别，这对被语音控制的智能语音应用来说极为危险。

消失的命令：使用声音掩蔽对自动语音识别系统进行欺骗攻击

Disappeared Command: Spoofing Attack On Automatic Speech Recognition Systems with Sound Masking

Recognizing a particular command or a keyword, keyword spotting has been
widely used in many voice interfaces such as Amazon's Alexa and Google Home. In
order to recognize a set of keywords, most of the recent deep learning based
approaches use a neural network trained with a large number of samples to
identify certain pre-defined keywords. This restricts the system from
recognizing new, user-defined keywords. Therefore, we first formulate this
problem as a few-shot keyword spotting and approach it using metric learning.
To enable this research, we also synthesize and publish a Few-shot Google
Speech Commands dataset. We then propose a solution to the few-shot keyword
spotting problem using temporal and dilated convolutions on prototypical
networks. Our comparative experimental results demonstrate keyword spotting of
new keywords using just a small number of samples.

研究通过使用度量学习，以及选择临时和膨胀卷积等技术来解决关键词识别中的新词汇难题。同时，利用生成的数据集，可以使用少量样本准确识别新的用户自定义的关键词汇。