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Detection of speech events and speaker characteristics through photo-plethysmographic signal neural processing

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dc.contributor.author Cámbara, Guillermo
dc.contributor.author Luque, Jordi
dc.contributor.author Farrús, Mireia
dc.date.accessioned 2020-05-07T08:00:59Z
dc.date.issued 2020
dc.identifier.citation Cámbara G, Luque J, Farrús M. Detection of speech events and speaker characteristics through photo-plethysmographic signal neural processing. In: 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Proceedings; 2020 May 4-8; Barcelona, Spain. New York: IEEE; 2020. p. 7564-8. DOI: 10.1109/ICASSP40776.2020.9052972
dc.identifier.isbn 978-1-5090-6631-5
dc.identifier.issn 1520-6149
dc.identifier.uri http://hdl.handle.net/10230/44443
dc.description Comunicació presentada a: 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), celebrat de manera virtual a Barcelona del 4 al 8 de maig de 2020.
dc.description.abstract The use of photoplethysmogram signal (PPG) for heart and sleep monitoring is commonly found nowadays in smart-phones and wrist wearables. Besides common usages, it has been proposed and reported that person information can be extracted from PPG for other uses, like biometry tasks. In this work, we explore several end-to-end convolutional neural network architectures for detection of human’s characteristics such as gender or person identity. In addition, we evaluate whether speech/non-speech events may be inferred from PPG signal, where speech might translate in fluctuations into the pulse signal. The obtained results are promising and clearly show the potential of fully end-to-end topologies for automatic extraction of meaningful biomarkers, even from a noisy signal sampled by a low-cost PPG sensor. The AUCs for best architectures put forward PPG wave as biological discriminant, reaching 79% and 89.0%, respectively for gender and person verification tasks. Furthermore, speech detection experiments reporting AUCs around 69% encourage us for further exploration about the feasibility of PPG for speech processing tasks.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Proceedings; 2020 May 4-8; Barcelona, Spain. New York: IEEE; 2020. p. 7564-8.
dc.rights © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/ICASSP40776.2020.9052972
dc.title Detection of speech events and speaker characteristics through photo-plethysmographic signal neural processing
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi http://dx.doi.org/10.1109/ICASSP40776.2020.9052972
dc.subject.keyword Photoplethysmogram signal
dc.subject.keyword PPG
dc.subject.keyword Speech detection
dc.subject.keyword Convolutional neural networks
dc.subject.keyword Biometric authentication
dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/RYC-2015-17239
dc.rights.accessRights info:eu-repo/semantics/embargoedAccess
dc.type.version info:eu-repo/semantics/acceptedVersion
dc.embargo.liftdate 2020-11-04
dc.date.embargoEnd info:eu-repo/date/embargoEnd/2020-11-04

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