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dc.contributor.authorRavenscroft, Dafydd
dc.contributor.authorPrattis, Ioannis
dc.contributor.authorKandukuri, Tharun
dc.contributor.authorSamad, Yarjan Abdul
dc.contributor.authorMallia, Giorgio
dc.contributor.authorOcchipinti, Luigi
dc.date.accessioned2022-01-10T12:45:27Z
dc.date.available2022-01-10T12:45:27Z
dc.date.issued2021-12-31
dc.identifier.issn1424-8220
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/332483
dc.description.abstractSilent speech recognition is the ability to recognise intended speech without audio information. Useful applications can be found in situations where sound waves are not produced or cannot be heard. Examples include speakers with physical voice impairments or environments in which audio transference is not reliable or secure. Developing a device which can detect non-auditory signals and map them to intended phonation could be used to develop a device to assist in such situations. In this work, we propose a graphene-based strain gauge sensor which can be worn on the throat and detect small muscle movements and vibrations. Machine learning algorithms then decode the non-audio signals and create a prediction on intended speech. The proposed strain gauge sensor is highly wearable, utilising graphene's unique and beneficial properties including strength, flexibility and high conductivity. A highly flexible and wearable sensor able to pick up small throat movements is fabricated by screen printing graphene onto lycra fabric. A framework for interpreting this information is proposed which explores the use of several machine learning techniques to predict intended words from the signals. A dataset of 15 unique words and four movements, each with 20 repetitions, was developed and used for the training of the machine learning algorithms. The results demonstrate the ability for such sensors to be able to predict spoken words. We produced a word accuracy rate of 55% on the word dataset and 85% on the movements dataset. This work demonstrates a proof-of-concept for the viability of combining a highly wearable graphene strain gauge and machine leaning methods to automate silent speech recognition.
dc.description.sponsorshipEP/S023046/1
dc.languageen
dc.publisherMDPI AG
dc.subjectartificial neural networks
dc.subjectgraphene
dc.subjectmachine learning
dc.subjectsilent speech recognition
dc.subjectstrain gauge
dc.titleMachine Learning Methods for Automatic Silent Speech Recognition Using a Wearable Graphene Strain Gauge Sensor.
dc.typeArticle
dc.date.updated2022-01-10T12:45:26Z
prism.issueIdentifier1
prism.publicationNameSensors (Basel)
prism.volume22
dc.identifier.doi10.17863/CAM.79933
dcterms.dateAccepted2021-12-28
rioxxterms.versionofrecord10.3390/s22010299
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidOcchipinti, Luigi [0000-0002-9067-2534]
dc.identifier.eissn1424-8220
pubs.funder-project-idEngineering and Physical Sciences Research Council (2262191)
pubs.funder-project-idEPSRC (EP/S023046/1)
cam.issuedOnline2021-12-31


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