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
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.
Metadata
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 Institutes 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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