Unvoiced Speech Recognition Using Tissue-Conductive Acoustic Sensor

Heracleous, Panikos; Kaino, Tomomi; Saruwatari, Hiroshi; Shikano, Kiyohiro

doi:10.1155/2007/94068

Cited by 24 publications

(11 citation statements)

References 17 publications

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“…3. Digital transformation of signals from a Non-Audible Murmur (NAM) microphone (a type of stethoscopic microphone) (Nakajima et al (2003a-b); Nakajima (2005); ACCEPTED MANUSCRIPT Nakajima et al (2006); Heracleous et al (2007); Otani et al (2008); Hirahara et al (2009, this issue); Tran et al (2008a-b); Tran et al (2009, this issue)); 4. Analysis of glottal activity using electromagnetic (Titze et al (2000); Ng et al (2000); Tardelli Ed.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

Silent speech interfaces

Denby¹,

Schultz²,

Honda³

et al. 2010

Speech Communication

414

230

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show abstract

“…In 2006 "Unvoiced speech recognition using Tissue conductive acoustic sensor" they displayed non capable of being heard mumble acknowledgment in clean and loud situations utilizing NAM mouthpieces. [8] A NAM receiver is a uncommon acoustic gadget connected behind the talker's ear, which can capture exceptionally discreetly expressed discourse. Non capable of being heard mumble acknowledgment can be utilized when privacy in human-machine communication is wanted.…”

Section: Literature Surveymentioning

confidence: 99%

Conversion of Body Conducted Unvoiced Speech(Murmur) to Normal Speech Using Hidden Markov Model (HMM)

RajeshKumar¹,

Srinagamani²,

chandu³

et al. 2018

IJET

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The main purpose of this paper is Conversion of non- audible murmured voice into the normal speech using Hidden Markov Model(HMM).This non audible murmur voice NAM is a one type of murmured voice which can be delivered by a NAM microphone which is attached behind the speaker’s ear. The Hidden Markov Models(HMMs) are stochastic models of statistical learning .These are very useful in speech recognition .The point of the paper is to collect as much as data from the device and convert it into audible and clear data signal that can be used for further sensory based applications. Hence, having an insight of how to convert the NAM to speech and then to whisper has a lot of benefits while keeping in mind the disadvantages of such conversion. Since, NAM is minute details of a communication between one’s own self it is highly recommended to the data in as much as discrete format as necessary since a speech signal can have various frequencies over a portion of the signal, big data approach is recommended.

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“…Ultrasound and optical imaging of tongue and lips [6,8,17,18] 3. Non-Audible Murmur microphone [15,16,26] 4. Electromagnetic and vibrational sensors of glottal activiy [27,28,32,36] 5.…”

Section: Related Workmentioning

confidence: 99%

The tongue and ear interface

Sahni

Bedri

Reyes

et al. 2014

Proceedings of the 2014 ACM International Symposium on Wearable Computers

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We address the problem of performing silent speech recognition where vocalized audio is not available (e.g. due to a user's medical condition) or is highly noisy (e.g. during firefighting or combat). We describe our wearable system to capture tongue and jaw movements during silent speech. The system has two components: the Tongue Magnet Interface (TMI), which utilizes the 3-axis magnetometer aboard Google Glass to measure the movement of a small magnet glued to the user's tongue, and the Outer Ear Interface (OEI), which measures the deformation in the ear canal caused by jaw movements using proximity sensors embedded in a set of earmolds. We collected a data set of 1901 utterances of 11 distinct phrases silently mouthed by six able-bodied participants. Recognition relies on using hidden Markov modelbased techniques to select one of the 11 phrases. We present encouraging results for user dependent recognition.

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Unvoiced Speech Recognition Using Tissue-Conductive Acoustic Sensor

Cited by 24 publications

References 17 publications

Silent speech interfaces

Silent speech interfaces

Conversion of Body Conducted Unvoiced Speech(Murmur) to Normal Speech Using Hidden Markov Model (HMM)

The tongue and ear interface

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