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Voxento 2.0: a prototype voice-controlled interactive search engine for lifelogs

Alateeq, Ahmed ORCID: 0000-0001-7916-6393, Mark, Roantree ORCID: 0000-0002-1329-2570 and Gurrin, Cathal ORCID: 0000-0003-2903-3968 (2021) Voxento 2.0: a prototype voice-controlled interactive search engine for lifelogs. In: ICMR '21: International Conference on Multimedia Retrieval, 21 Aug 2021, Taipei Taiwan. ISBN 978-1-4503-8533-6

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Abstract

In this paper, we describe an extended version of Voxento which is an interactive voice-based retrieval system for lifelogs that has been developed to participate in the fourth Lifelog Search Challenge LSC’21, at ACM ICMR’21. Voxento provides a spoken interface to the lifelog dataset, which facilitates a novice user to interact with a personal lifelog using a range of vocal commands and interactions. For the version presented here, Voxento has been enhanced with new retrieval features and better user interaction support. In this paper, we introduce these new features, which include dynamic result filtering, predefined interactive responses and the development of a new retrieval API. Although Voxento was proposed for wearable technologies such as Google Glass or interactive devices like smart TVs, the version of Voxento presented here uses a desktop computer in order to participate in the LSC’21 competition. In the current Voxento iteration, the user has the option to enable voice interaction or use standard text-based retrieval.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:lifelog; interactive retrieval; voice interaction; speech recognition; speech synthesis
Subjects:Computer Science > Information retrieval
Computer Science > Lifelog
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: Proceedings of the 4th Annual Workshop on Lifelog Search Challenge (LSC'21). . Association for Computing Machinery (ACM). ISBN 978-1-4503-8533-6
Publisher:Association for Computing Machinery (ACM)
Official URL:https://dx.doi.org/10.1145/3463948.3469071
Copyright Information:© 2021 The Authors. Open Access
Funders:Science Foundation Ireland and the Insight Centre for Data Analytics through the grant number SFI/12/RC/2289-P2
ID Code:26525
Deposited On:10 Dec 2021 12:48 by Ahmed Alateeq . Last Modified 12 May 2022 15:57

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