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dc.contributor.authorGonzález López, José Andrés 
dc.contributor.authorGómez Alanís, Alejandro 
dc.contributor.authorPérez Córdoba, José Luis 
dc.contributor.authorGreen, Phil D.
dc.date.accessioned2022-01-25T07:39:39Z
dc.date.available2022-01-25T07:39:39Z
dc.date.issued2022-01-23
dc.identifier.citationGonzalez-Lopez, J.A.; Gomez-Alanis, A.; Pérez-Córdoba, J.L.; Green, P.D. Non-Parallel Articulatory-to-Acoustic Conversion Using Multiview-Based Time Warping. Appl. Sci. 2022, 12, 1167. [https://doi.org/10.3390/app12031167]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/72464
dc.descriptionThis work was supported in part by the Spanish State Research Agency (SRA) grant number PID2019-108040RB-C22/SRA/10.13039/501100011033, and the FEDER/Junta de AndalucíaConsejería de Transformación Económica, Industria, Conocimiento y Universidades project no. B-SEJ-570-UGR20.es_ES
dc.description.abstractIn this paper, we propose a novel algorithm called multiview temporal alignment by dependence maximisation in the latent space (TRANSIENCE) for the alignment of time series consisting of sequences of feature vectors with different length and dimensionality of the feature vectors. The proposed algorithm, which is based on the theory of multiview learning, can be seen as an extension of the well-known dynamic time warping (DTW) algorithm but, as mentioned, it allows the sequences to have different dimensionalities. Our algorithm attempts to find an optimal temporal alignment between pairs of nonaligned sequences by first projecting their feature vectors into a common latent space where both views are maximally similar. To do this, powerful, nonlinear deep neural network (DNN) models are employed. Then, the resulting sequences of embedding vectors are aligned using DTW. Finally, the alignment paths obtained in the previous step are applied to the original sequences to align them. In the paper, we explore several variants of the algorithm that mainly differ in the way the DNNs are trained. We evaluated the proposed algorithm on a articulatory-to-acoustic (A2A) synthesis task involving the generation of audible speech from motion data captured from the lips and tongue of healthy speakers using a technique known as permanent magnet articulography (PMA). In this task, our algorithm is applied during the training stage to align pairs of nonaligned speech and PMA recordings that are later used to train DNNs able to synthesis speech from PMA data. Our results show the quality of speech generated in the nonaligned scenario is comparable to that obtained in the parallel scenario.es_ES
dc.description.sponsorshipSpanish State Research Agency (SRA) PID2019-108040RB-C22/SRA/10.13039/501100011033es_ES
dc.description.sponsorshipFEDER/Junta de AndalucíaConsejería de Transformación Económica, Industria, Conocimiento y Universidades project no. B-SEJ-570-UGR20.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 3.0 España
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subjectDeep learninges_ES
dc.subjectMultiview learninges_ES
dc.subjectDynamic time warpinges_ES
dc.subjectCanonical correlation analysises_ES
dc.subjectSilent speech interfacees_ES
dc.subjectLatent embeddinges_ES
dc.titleNon-Parallel Articulatory-to-Acoustic Conversion Using Multiview-based Time Warpinges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/app12031167
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones_ES


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España