Conference Paper/Proceeding/Abstract 1346 views 210 downloads
Opportunities and Challenges of Automatic Speech Recognition Systems for Low-Resource Language Speakers
,
Electra Wallington,
Dani Kalarikalayil Raju,
Ondrej Klejch,
Jennifer Pearson,
Matt Jones ,
Peter Bell,
Simon Robinson ,
Jen Pearson
CHI Conference on Human Factors in Computing Systems (CHI '22), April 29–May 5, 2022, New Orleans, LA, USA. ACM, New York, NY, USA, Pages: 1 - 17
Swansea University Authors:
Thomas Reitmaier , Matt Jones , Simon Robinson , Jen Pearson
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DOI (Published version): 10.1145/3491102.3517639
Abstract
Automatic Speech Recognition (ASR) researchers are turning their attention towards supporting low-resource languages, such as isiXhosa or Marathi, with only limited training resources. We report and reflect on collaborative research across ASR & HCI to situate ASR-enabled technologies to suit th...
| Published in: | CHI Conference on Human Factors in Computing Systems (CHI '22), April 29–May 5, 2022, New Orleans, LA, USA. ACM, New York, NY, USA |
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| ISBN: | 978-1-4503-9157-3 |
| Published: |
New York, NY, USA
ACM Digital Library
2022
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa59573 |
| Abstract: |
Automatic Speech Recognition (ASR) researchers are turning their attention towards supporting low-resource languages, such as isiXhosa or Marathi, with only limited training resources. We report and reflect on collaborative research across ASR & HCI to situate ASR-enabled technologies to suit the needs and functions of two communities of low-resource language speakers, on the outskirts of Cape Town, South Africa and in Mumbai, India. We build on longstanding community partnerships and draw on linguistics, media studies and HCI scholarship to guide our research. We demonstrate diverse design methods to: remotely engage participants; collect speech data to test ASR models; and ultimately field-test models with users. Reflecting on the research, we identify opportunities, challenges, and use-cases of ASR, in particular to support pervasive use of WhatsApp voice messaging. Finally, we uncover implications for collaborations across ASR & HCI that advance important discussions at CHI surrounding data, ethics, and AI. |
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| Keywords: |
Speech/language, automatic speech recognition, mobile devices |
| College: |
Faculty of Science and Engineering |
| Funders: |
UKRI |
| Start Page: |
1 |
| End Page: |
17 |