@inproceedings{jones-2020-macsen,
title = "{M}acsen: A Voice Assistant for Speakers of a Lesser Resourced Language",
author = "Jones, Dewi",
editor = "Beermann, Dorothee and
Besacier, Laurent and
Sakti, Sakriani and
Soria, Claudia",
booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources association",
url = "https://aclanthology.org/2020.sltu-1.27",
pages = "194--201",
abstract = "This paper reports on the development of a voice assistant mobile app for speakers of a lesser resourced language {--} Welsh. An assistant with a smaller set of effective but useful skills is both desirable and urgent for the wider Welsh speaking community. Descriptions of the app{'}s skills, architecture, design decisions and user interface is provided before elaborating on the most recent research and activities in open source speech technology for Welsh. The paper reports on the progress to date on crowdsourcing Welsh speech data in Mozilla Common Voice and of its suitability for training Mozilla{'}s DeepSpeech speech recognition for a voice assistant application according to conventional and transfer learning methods. We demonstrate that with smaller datasets of speech data, transfer learning and a domain specific language model, acceptable speech recognition is achievable that facilitates, as confirmed by beta users, a practical and useful voice assistant for Welsh speakers. We hope that this work informs and serves as a model to researchers and developers in other lesser-resourced linguistic communities and helps bring into being voice assistant apps for their languages.",
language = "English",
ISBN = "979-10-95546-35-1",
}
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<abstract>This paper reports on the development of a voice assistant mobile app for speakers of a lesser resourced language – Welsh. An assistant with a smaller set of effective but useful skills is both desirable and urgent for the wider Welsh speaking community. Descriptions of the app’s skills, architecture, design decisions and user interface is provided before elaborating on the most recent research and activities in open source speech technology for Welsh. The paper reports on the progress to date on crowdsourcing Welsh speech data in Mozilla Common Voice and of its suitability for training Mozilla’s DeepSpeech speech recognition for a voice assistant application according to conventional and transfer learning methods. We demonstrate that with smaller datasets of speech data, transfer learning and a domain specific language model, acceptable speech recognition is achievable that facilitates, as confirmed by beta users, a practical and useful voice assistant for Welsh speakers. We hope that this work informs and serves as a model to researchers and developers in other lesser-resourced linguistic communities and helps bring into being voice assistant apps for their languages.</abstract>
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%0 Conference Proceedings
%T Macsen: A Voice Assistant for Speakers of a Lesser Resourced Language
%A Jones, Dewi
%Y Beermann, Dorothee
%Y Besacier, Laurent
%Y Sakti, Sakriani
%Y Soria, Claudia
%S Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)
%D 2020
%8 May
%I European Language Resources association
%C Marseille, France
%@ 979-10-95546-35-1
%G English
%F jones-2020-macsen
%X This paper reports on the development of a voice assistant mobile app for speakers of a lesser resourced language – Welsh. An assistant with a smaller set of effective but useful skills is both desirable and urgent for the wider Welsh speaking community. Descriptions of the app’s skills, architecture, design decisions and user interface is provided before elaborating on the most recent research and activities in open source speech technology for Welsh. The paper reports on the progress to date on crowdsourcing Welsh speech data in Mozilla Common Voice and of its suitability for training Mozilla’s DeepSpeech speech recognition for a voice assistant application according to conventional and transfer learning methods. We demonstrate that with smaller datasets of speech data, transfer learning and a domain specific language model, acceptable speech recognition is achievable that facilitates, as confirmed by beta users, a practical and useful voice assistant for Welsh speakers. We hope that this work informs and serves as a model to researchers and developers in other lesser-resourced linguistic communities and helps bring into being voice assistant apps for their languages.
%U https://aclanthology.org/2020.sltu-1.27
%P 194-201
Markdown (Informal)
[Macsen: A Voice Assistant for Speakers of a Lesser Resourced Language](https://aclanthology.org/2020.sltu-1.27) (Jones, SLTU 2020)
ACL