Breaking Barriers for Minority Languages: Development and Evaluation of the First Galician On-Device ASR Model for Metaverse Applications

UDC.coleccionInvestigaciónes_ES
UDC.conferenceTitleICIR 2024es_ES
UDC.departamentoEnxeñaría de Computadoreses_ES
UDC.grupoInvGrupo de Tecnoloxía Electrónica e Comunicacións (GTEC)es_ES
UDC.institutoCentroCITIC - Centro de Investigación de Tecnoloxías da Información e da Comunicaciónes_ES
dc.contributor.authorValladares Poncela, Antón
dc.contributor.authorFraga-Lamas, Paula
dc.contributor.authorFernández-Caramés, Tiago M.
dc.date.accessioned2025-02-25T08:48:18Z
dc.date.available2025-02-25T08:48:18Z
dc.date.issued2024
dc.descriptionPresented at: 2024 IEEE 3rd International Conference on Intelligent Reality (ICIR 2024), 5-6 December 2024, Coimbra (Portugal)es_ES
dc.description.abstract[Abstract]: This paper presents the development and evaluation of the first Automatic Speech Recognition (ASR) model for the Galician language, specifically designed for Metaverse applications using Microsoft HoloLens 2 smart glasses. The Metaverse is an expanding digital space for immersive interactions using different Extended Reality (XR) technologies, where voice-based communication is essential for an intuitive User Experience (UX). For minority languages, voice interaction is especially valuable as it promotes accessibility in this new environment by removing linguistic barriers. While ASR systems for highresource languages like English and Mandarin are becoming increasingly common in the Metaverse, there are no existing models optimized for Metaverse applications for Galician, a lowresource language spoken by approximately 3.2 million people. The developed ASR system leverages a fine-tuned Wav2Vec 2.0 model optimized specifically for on-device inference in ARM64 architectures, ensuring low latency without relying on cloudbased solutions. Performance evaluations indicate a Character Error Rate (CER) below 6.7 % and an average latency of 2.98 seconds, demonstrating the model effectiveness in realtime transcription tasks in computationally constrained devices such as the Microsoft HoloLens 2. Thus, this paper lays the groundwork for the future integration of Galician and other minority languages in the development of Metaverse applications.es_ES
dc.description.sponsorshipThis work has been funded by grant TED2021-129433A-C22 (HELENE) funded by MCIN/AEI/10.13039/501100011033 and the European Union NextGenerationEU/PRTR.es_ES
dc.identifier.citationA. Valladares-Poncela, P. Fraga-Lamas, and T. M. Fernandez-Caramés, "Breaking Barriers for Minority Languages: Development and Evaluation of the First Galician On-Device ASR Model for Metaverse Applications", 2024 IEEE 3rd International Conference on Intelligent Reality (ICIR 2024), 5-6 December 2024, Coimbra (Portugal)es_ES
dc.identifier.urihttp://hdl.handle.net/2183/41259
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-129433A-C22/ES/SISTEMA DE ALTA SEGURIDAD BASADO EN BLOCKCHAIN PARA LA GESTIÓN PRIVADA DE DATOS DE PACIENTES DE SERVICIOS DE SALUD DIGITALESes_ES
dc.relation.urihttps://icir.ieee.org/2024-program/es_ES
dc.rights© 2024 IEEE. This is the accepted version, after peer review. 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.es_ES
dc.rights.accessRightsopen accesses_ES
dc.subjectGalicianes_ES
dc.subjectASRes_ES
dc.subjectMachine learninges_ES
dc.subjectMixed realityes_ES
dc.subjectMetaversees_ES
dc.subjectLow-resource languageses_ES
dc.titleBreaking Barriers for Minority Languages: Development and Evaluation of the First Galician On-Device ASR Model for Metaverse Applicationses_ES
dc.typeconference outputes_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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