基于超声成像的深度神经网络静音语音交互

N. Kimura, Michinari Kono, J. Rekimoto
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引用次数: 87

摘要

通过语音操作的数字设备的可用性正在迅速扩大。然而,语音接口的应用仍然受到限制。例如,在公共场合讲话会让周围的人感到厌烦,不应该说出秘密信息。环境噪声会降低语音识别的准确性。为了解决这些限制,提出了一种检测用户未发音话语的系统。通过安装在下颚下方的超声波成像传感器观察到的内部信息,我们提出的系统可以在用户不发出声音的情况下识别话语内容。我们提出的深度神经网络模型用于从一系列超声图像中获取声学特征。我们确认系统产生的音频信号可以控制现有的智能音箱。我们还观察到,用户可以调整他们的口腔运动来学习和提高他们语音识别的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SottoVoce: An Ultrasound Imaging-Based Silent Speech Interaction Using Deep Neural Networks
The availability of digital devices operated by voice is expanding rapidly. However, the applications of voice interfaces are still restricted. For example, speaking in public places becomes an annoyance to the surrounding people, and secret information should not be uttered. Environmental noise may reduce the accuracy of speech recognition. To address these limitations, a system to detect a user's unvoiced utterance is proposed. From internal information observed by an ultrasonic imaging sensor attached to the underside of the jaw, our proposed system recognizes the utterance contents without the user's uttering voice. Our proposed deep neural network model is used to obtain acoustic features from a sequence of ultrasound images. We confirmed that audio signals generated by our system can control the existing smart speakers. We also observed that a user can adjust their oral movement to learn and improve the accuracy of their voice recognition.
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