Conference Paper/Proceeding/Abstract 546 views 57 downloads
Situating Automatic Speech Recognition Development within Communities of Under-heard Language Speakers
,
Matt Jones ,
Peter Bell,
Simon Robinson
ACM CHI Conference on Human Factors in Computing Systems: CHI' 23, Pages: 1 - 17
Swansea University Authors:
Thomas Reitmaier, Jen Pearson , Matt Jones , Simon Robinson
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DOI (Published version): 10.1145/3544548.3581385
Abstract
In this paper we develop approaches to automatic speech recognition (ASR) development that suit the needs and functions of underheard language speakers. Our novel contribution to HCI is to show how community-engagement can surface key technical and social issues and opportunities for more efective s...
| Published in: | ACM CHI Conference on Human Factors in Computing Systems: CHI' 23 |
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| ISBN: | 978-1-4503-9421-5/23/04 |
| Published: |
ACM
2023
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa62395 |
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| Abstract: |
In this paper we develop approaches to automatic speech recognition (ASR) development that suit the needs and functions of underheard language speakers. Our novel contribution to HCI is to show how community-engagement can surface key technical and social issues and opportunities for more efective speech-based systems. We introduce a bespoke toolkit of technologies and showcase how we utilised the toolkit to engage communities of under-heard language speakers; and, through that engagement process, situate key aspects of ASR development in community contexts. The toolkit consists of (1) an information appliance to facilitate spoken-data collection on topics of community interest, (2) a mobile app to create crowdsourced transcripts of collected data, and (3) demonstrator systems to showcase ASR capabilities and to feed back research results to community members. Drawing on the sensibilities we cultivated through this research, we present a series of challenges to the orthodoxy of state-of-the-art approaches to ASR development. |
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| Keywords: |
Text/speech/language, automatic speech recognition, mobile devices: phones/tablets |
| College: |
Faculty of Science and Engineering |
| Funders: |
UKRI (EP/T024976/1) |
| Start Page: |
1 |
| End Page: |
17 |