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Mouth State Detection From Low-Frequency Ultrasonic Reflection

McLoughlin, Ian Vince, Song, Yan (2014) Mouth State Detection From Low-Frequency Ultrasonic Reflection. Circuits, Systems, and Signal Processing, 34 (4). pp. 1279-1304. ISSN 0278-081X. E-ISSN 1531-5878. (doi:10.1007/s00034-014-9904-4) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:48872)

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This paper develops, simulates and experimentally evaluates a novel method based on non-contact low frequency (LF) ultrasound which can determine, from airborne reflection, whether the lips of a subject are open or closed. The method is capable of accurately distinguishing between open and closed lip states through the use of a low-complexity detection algorithm, and is highly robust to interfering audible noise. A novel voice activity detector is implemented and evaluated using the proposed method and shown to detect voice activity with high accuracy, even in the presence of high levels of background noise. The lip state detector is evaluated at a number of angles of incidence to the mouth and under various conditions of background noise. The underlying mouth state detection technique relies upon an inaudible LF ultrasonic excitation, generated in front of the face of a user, either reflecting back from their face as a simple echo in the closed mouth state or resonating inside the open mouth and vocal tract, affecting the spectral response of the reflected wave when the mouth is open. The difference between echo and resonance behaviours is used as the basis for automated lip opening detection, which implies determining whether the mouth is open or closed at the lips. Apart from this, potential applications include use in voice generation prosthesis for speech impaired patients, or as a hands-free control for electrolarynx and similar rehabilitation devices. It is also applicable to silent speech interfaces and may have use for speech authentication.

Item Type: Article
DOI/Identification number: 10.1007/s00034-014-9904-4
Subjects: T Technology
Divisions: Faculties > Sciences > School of Computing
Depositing User: Ian McLoughlin
Date Deposited: 25 Aug 2015 08:38 UTC
Last Modified: 29 May 2019 14:40 UTC
Resource URI: (The current URI for this page, for reference purposes)
McLoughlin, Ian Vince:
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