A novel non-acoustic voiced speech sensor

Brown, Donald R.; Ludwig, Reinhold; Pelteku, Altin E.; Bogdanov, G. P.; Keenaghan, K

doi:10.1088/0957-0233/15/7/010

Cited by 4 publications

(15 citation statements)

References 17 publications

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“…As with the EGG, antenna positioning is a crucial factor for GEMS.TERC. The Tuned Electromagnetic Resonating Collar(Brown et al (2004);Brown et al (2005); TERC (2009)) measures changes in the intrinsic electrical capacitance of the glottis.The device exploits the fact that when the glottis opens, the permittivity of a cross section of the neck through the larynx decreases. The device consists of a neckband composed of copper electrodes on an acrylic substrate, tuned to a sharp resonance at a particular frequency of several tens of MHz.…”

mentioning

confidence: 99%

Silent speech interfaces

Denby¹,

Schultz²,

Honda³

et al. 2010

Speech Communication

414

230

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International audienceThe possibility of speech processing in the absence of an intelligible acoustic signal has given rise to the idea of a 'silent speech' interface, to be used as an aid for the speech-handicapped, or as part of a communications system operating in silence-required or high-background-noise environments. The article first outlines the emergence of the silent speech interface from the fields of speech production, automatic speech processing, speech pathology research, and telecommunications privacy issues, and then follows with a presentation of demonstrator systems based on seven different types of technologies. A concluding section underlining some of the common challenges faced by silent speech interface researchers, and ideas for possible future directions, is also provided

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mentioning

confidence: 99%

Silent speech interfaces

Denby¹,

Schultz²,

Honda³

et al. 2010

Speech Communication

414

230

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“…Work has been done to improve signal processing and speech recognition for surface-EMG [16]. Some of this work has begun to integrate small, portable EMG sensors, like the one we use, with prosthetic devices [3], [14], [15]. Our work focuses instead on improving performance by changing the dynamic response of the prosthetic device, rather than by enhancing the recognition of words.…”

Section: B Communication Using Surface Electromyographymentioning

confidence: 99%

Policies for neural prosthetic control: Initial experiments with a text interface

Omar

Johnson

Coleman

2008

2008 American Control Conference

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Neural interfaces use estimates of brain or muscle activity to generate control inputs for a prosthetic device. Most previous work focuses on estimating neural activity more accurately. This paper focuses on generating better control inputs. It shows that changing the dynamic response of a prosthetic device can make specific tasks easier to accomplish. It also presents experimental results for which neural activity is measured using surface electromyography, the prosthetic is a 1-D cursor, and the task is to spell words from a menu of characters.

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“…[10][11][12]). While these techniques give good recognition rates (typically 90% for continuous speech), they are not perfect, particularly in noisy environments and so consideration has been given to the augmentation of acoustic signals by other measurements [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Development of a (silent) speech recognition system for patients following laryngectomy

Fagan

Ell

Gilbert

et al. 2008

Medical Engineering & Physics

130

111

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Surgical voice restoration post-laryngectomy has a number of limitations and drawbacks. The present gold standard involves the use of a tracheo-oesophageal fistula (TOF) valve to divert air from the lungs into the throat, which vibrates, and from this, speech can be formed. Not all patients can use these valves and those who do are susceptible to complications associated with valve failure. Thus there is still a place for other voice restoration options. With advances in electronic miniaturization and portable computing power a computing-intensive solution has been investigated. Magnets were placed on the lips, teeth and tongue of a volunteer causing a change in the surrounding magnetic field when the individual mouthed words. These changes were detected by 6 dual axis magnetic sensors, which were incorporated into a pair of special glasses. The resulting signals were compared to training data recorded previously by means of a dynamic time warping algorithm using dynamic programming. When compared to a small vocabulary database, the patterns were found to be recognised with an accuracy of 97% for words and 94% for phonemes. On this basis we plan to develop a speech system for patients who have lost laryngeal function.

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A novel non-acoustic voiced speech sensor

Cited by 4 publications

References 17 publications

Silent speech interfaces

Silent speech interfaces

Policies for neural prosthetic control: Initial experiments with a text interface

Development of a (silent) speech recognition system for patients following laryngectomy

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