WaveEar: Exploring a mmWave-based Noise-resistant Speech Sensing for Voice-User Interface
WaveEar: Exploring a mmWave-based Noise-resistant Speech Sensing for Voice-User Interface
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DOI：
10.1145/3307334.3326073
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发表时间：
2019-06
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通讯作者：
Chenhan Xu;Zhengxiong Li;Hanbin Zhang;Aditya Singh Rathore;Huining Li;Chen Song;Kun Wang;Wenyao Xu
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文献类型：
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作者：
Chenhan Xu;Zhengxiong Li;Hanbin Zhang;Aditya Singh Rathore;Huining Li;Chen Song;Kun Wang;Wenyao Xu
语音用户界面（VUI）通过从根本上改变用户与设备之间的信息共享方式，已成为现代个人设备（例如智能手机、语音助手）中不可或缺的组成部分。用于VUI的声学传感旨在感知所有声学对象；然而，现有的VUI机制只能提供低质量的语音传感。这是由于复杂环境噪声的可听和不可听干扰，这些干扰通过导致用户请求的拒绝服务（DoS）而限制了VUI的性能。因此，在VUI中实现抗噪语音传感，以便在复杂环境中高效、精确地执行关键任务至关重要。为此，我们研究了使用射频信号（例如毫米波（mmWave））来感知个人抗噪语音的可行性。我们首先对语音产生的原理以及由此产生的声带振动进行了深入研究。基于所获得的见解，我们提出了WaveEar，一种端到端的抗噪语音传感系统。WaveEar包括一个低成本的毫米波探头，用于在多人中定位说话者的位置，并将毫米波信号导向说话者的喉部附近区域以感知其声带振动。包含语音信息的接收信号被送入我们新颖的深度神经网络，通过详尽的提取来恢复语音。我们在有21名参与者的真实场景下进行的实验评估表明，WaveEar能够有效准确地推断抗噪语音，并使现代电子设备中实现普遍的VUI。
Voice-user interface (VUI) has become an integral component in modern personal devices (\textite.g., smartphones, voice assistant) by fundamentally evolving the information sharing between the user and device. Acoustic sensing for VUI is designed to sense all acoustic objects; however, the existing VUI mechanism can only offer low-quality speech sensing. This is due to the audible and inaudible interference from complex ambient noise that limits the performance of VUI by causing denial-of-service (DoS) of user requests. Therefore, it is of paramount importance to enable noise-resistant speech sensing in VUI for executing critical tasks with superior efficiency and precision in robust environments. To this end, we investigate the feasibility of employing radio-frequency signals, such as millimeter wave (mmWave) for sensing the noise-resistant voice of an individual. We first perform an in-depth study behind the rationale of voice generation and resulting vocal vibrations. From the obtained insights, we presentWaveEar, an end-to-end noise-resistant speech sensing system.WaveEar comprises a low-cost mmWave probe to localize the position of the speaker among multiple people and direct the mmWave signals towards the near-throat region of the speaker for sensing his/her vocal vibrations. The received signal, containing the speech information, is fed to our novel deep neural network for recovering the voice through exhaustive extraction. Our experimental evaluation under real-world scenarios with 21 participants shows the effectiveness ofWaveEar to precisely infer the noise-resistant voice and enable a pervasive VUI in modern electronic devices.