On-Device Automatic Speech Recognition for Low-Resource Languages in Mixed Reality Industrial Metaverse Applications: Practical Guidelines and Evaluation of a Shipbuilding Application in Galician

UDC.coleccionInvestigaciónes_ES
UDC.departamentoEnxeñaría de Computadoreses_ES
UDC.endPage77038es_ES
UDC.grupoInvGrupo de Tecnoloxía Electrónica e Comunicacións (GTEC)es_ES
UDC.institutoCentroCITIC - Centro de Investigación de Tecnoloxías da Información e da Comunicaciónes_ES
UDC.journalTitleIEEE Accesses_ES
UDC.startPage77017es_ES
UDC.volume13es_ES
dc.contributor.authorValladares Poncela, Antón
dc.contributor.authorFraga-Lamas, Paula
dc.contributor.authorFernández-Caramés, Tiago M.
dc.date.accessioned2025-05-13T14:16:44Z
dc.date.available2025-05-13T14:16:44Z
dc.date.issued2025-04
dc.description.abstract[Abstract]: As the Metaverse and Mixed Reality (MR) technologies continue to evolve, enabling natural and intuitive user interfaces is crucial. However, supporting low-resource languages in these advanced systems presents unique challenges. This article explores the development and deployment of an on-device Automatic Speech Recognition (ASR) system for Galician, a low-resource language spoken by less than 3 million people, implemented on the Microsoft HoloLens 2 MR glasses. The system prioritizes data privacy and security by eliminating the need for Internet connectivity or external processing. Key implementation choices, including software and libraries, are detailed, along with optimization strategies for minimizing latency. Performance evaluations, taking into account noise-simulated environments, demonstrate the high accuracy and low latency of the system, proving its effectiveness as an on-device ASR system for current and future Metaverse applications. In order to demonstrate the effectiveness of the developed system, it has been incorporated in an electrical outfitting application for Navantia, one of the largest shipbuilding companies in the world, illustrating its practical utility in an industrial scenario like a shipyard. The results obtained show a Character Error Rate (CER) below 6% and a latency of under 3 seconds using an ARM64 quantized model, which validates the effectiveness of the system for real-time voice control in industrial MR environments.es_ES
dc.description.sponsorshipThis work has been supported by Centro Mixto de Investigación UDC-NAVANTIA (IN853C 2022/01), funded by GAIN (Xunta de Galicia) and ERDF Galicia 2021-2027. Funding for open access charge: Universidade da Coruña/CISUG.es_ES
dc.description.sponsorshipXunta de Galicia; IN853C 2022/01es_ES
dc.description.sponsorshipFinanciado para publicación en acceso aberto: Universidade da Coruña/CISUGes_ES
dc.identifier.citationA. Valladares-Poncela, P. Fraga-Lamas and T. M. Fernández-Caramés, "On-Device Automatic Speech Recognition for Low-Resource Languages in Mixed Reality Industrial Metaverse Applications: Practical Guidelines and Evaluation of a Shipbuilding Application in Galician," in IEEE Access, vol. 13, pp. 77017-77038, 2025, doi: 10.1109/ACCESS.2025.3564137es_ES
dc.identifier.doi10.1109/ACCESS.2025.3564137
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/2183/41982
dc.language.isoenges_ES
dc.publisherInstitute of Electrical and Electronics Engineerses_ES
dc.relation.urihttps://doi.org/10.1109/ACCESS.2025.3564137es_ES
dc.rightsAtribución 4.0 Internacionales_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectAutomatic speech recognition (ASR)es_ES
dc.subjectExtended realityes_ES
dc.subjectIndustrial metaversees_ES
dc.subjectIoTes_ES
dc.subjectMicrosoft HoloLens 2es_ES
dc.subjectMixed Realityes_ES
dc.titleOn-Device Automatic Speech Recognition for Low-Resource Languages in Mixed Reality Industrial Metaverse Applications: Practical Guidelines and Evaluation of a Shipbuilding Application in Galicianes_ES
dc.typejournal articlees_ES
dc.type.hasVersionVoRes_ES
dspace.entity.typePublication
relation.isAuthorOfPublicationcaa923d2-cf88-405e-9025-759d06cf3799
relation.isAuthorOfPublication79dbfabd-7261-41ff-9667-2f774d5f341e
relation.isAuthorOfPublication.latestForDiscoverycaa923d2-cf88-405e-9025-759d06cf3799

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