A New Kind of Non-Acoustic Speech Acquisition Method Based on Millimeter Waveradar

Li, Sheng; Tian, Ying; Lü, Guohua; Yang, Zhigang; Xue, Hui; Wang, Jianqi; Jing, Xi-Jing

doi:10.2528/pier12052207

Cited by 18 publications

(19 citation statements)

References 42 publications

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“…The varying displacement of vibration, ( ) t  is linearly proportional to the varying phase of th signal: e echo ing antennas are both parabolic antennas with a diameter of 300 mm, and the estimated beam width is 9°. The voltage controlled oscillator generates a very stable MMW at 34.5 GHz with an output power of 100 mW [13]. This theoretical analysis suggests th ca ar sensor ar front end is shown in Figure 4.…”

Section: Theoretical Analysismentioning

confidence: 73%

Wireless Bioradar Sensor Networks for Speech Detection and Communication

Tian¹,

Li²,

Wang³

2013

ENG

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Wireless multimedia sensor networks (WMSN) are emerging to serve for the collection of acoustic and image information. In the WMSN, the microphone is usually employed to function as sensor nodes for the acquisition of acoustic data. However, those microphone sensors are needed to be placed close with sound source and cannot detect sound signal through certain obstacles. To overcome the shortcomings of microphone sensor, we develop a new type of bioradar sensor to achieve non-contact speech detection and investigate theoretically the mechanism of bioradar for speech detection. Results show that the system can successfully detect speech at some distance and even through non-metallic objects with certain thickness. In addition, in order to suppress the noise and improve the quality of the detected speech, we use spectral subtraction and Wiener filtering algorithm respectively to enhance the bioradar speech and evaluate the performance of the two methods using spectrogram

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Section: Theoretical Analysismentioning

confidence: 73%

Wireless Bioradar Sensor Networks for Speech Detection and Communication

Tian¹,

Li²,

Wang³

2013

ENG

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“…Such an approach has the advantage of a high level of directional sensitivity with penetration and a long detection distance. However, the radar speech itself has several serious shortcomings, which include artificial quality, reduced intelligibility, and poor audibility [5], [6]. The requirement of large and complicated hardware is another drawback of radar microphone techniques.…”

Section: Introductionmentioning

confidence: 99%

Silent Speech Interface Using Ultrasonic Doppler Sonar

Lee

2020

IEICE Trans. Inf. & Syst.

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Some non-acoustic modalities have the ability to reveal certain speech attributes that can be used for synthesizing speech signals without acoustic signals. This study validated the use of ultrasonic Doppler frequency shifts caused by facial movements to implement a silent speech interface system. A 40kHz ultrasonic beam is incident to a speaker's mouth region. The features derived from the demodulated received signals were used to estimate the speech parameters. A nonlinear regression approach was employed in this estimation where the relationship between ultrasonic features and corresponding speech is represented by deep neural networks (DNN). In this study, we investigated the discrepancies between the ultrasonic signals of audible and silent speech to validate the possibility for totally silent communication. Since reference speech signals are not available in silently mouthed ultrasonic signals, a nearest-neighbor search and alignment method was proposed, wherein alignment was achieved by determining the optimal pair of ultrasonic and audible features in the sense of a minimum mean square error criterion. The experimental results showed that the performance of the ultrasonic Doppler-based method was superior to that of EMG-based speech estimation, and was comparable to an imagebased method.

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“…As commonly used speech acquisition equipment, the microphone suffers from some serious shortcomings such as short detection distance, low directional sensitivity, high susceptibility to acoustic interference noise, and narrow acoustic frequency bandwidth [ 1 ]. Therefore, it is necessary to explore new types of speech acquisition methods.…”

Section: Introductionmentioning

confidence: 99%

A 94-GHz Millimeter-Wave Sensor for Speech Signal Acquisition

Tian

Lü

et al. 2013

Sensors

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High frequency millimeter-wave (MMW) radar-like sensors enable the detection of speech signals. This novel non-acoustic speech detection method has some special advantages not offered by traditional microphones, such as preventing strong-acoustic interference, high directional sensitivity with penetration, and long detection distance. A 94-GHz MMW radar sensor was employed in this study to test its speech acquisition ability. A 34-GHz zero intermediate frequency radar, a 34-GHz superheterodyne radar, and a microphone were also used for comparison purposes. A short-time phase-spectrum-compensation algorithm was used to enhance the detected speech. The results reveal that the 94-GHz radar sensor showed the highest sensitivity and obtained the highest speech quality subjective measurement score. This result suggests that the MMW radar sensor has better performance than a traditional microphone in terms of speech detection for detection distances longer than 1 m. As a substitute for the traditional speech acquisition method, this novel speech acquisition method demonstrates a large potential for many speech related applications.

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A New Kind of Non-Acoustic Speech Acquisition Method Based on Millimeter Waveradar

Cited by 18 publications

References 42 publications

Wireless Bioradar Sensor Networks for Speech Detection and Communication

Wireless Bioradar Sensor Networks for Speech Detection and Communication

Silent Speech Interface Using Ultrasonic Doppler Sonar

A 94-GHz Millimeter-Wave Sensor for Speech Signal Acquisition

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