Opportunities and Challenges of Automatic Speech Recognition Systems for Low-Resource Language ...

Opportunities and Challenges of Automatic Speech Recognition Systems for Low-Resource Language Speakers
Thomas Reitmaier, Electra Wallington, Dani Kalarikalayil Raju, Ondrej Klejch, Jennifer Pearson, Matt Jones, Peter Bell, Simon Robinson

CHI'22: ACM Conference on Human Factors in Computing Systems
Session: Technology for Developing Regions and Underserved Populations

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 long standing 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 VoiceNotes. Finally, we uncover implications for collaborations across ASR & HCI that advance important discussions at CHI surrounding data, ethics, and AI.

Pre-recorded presentations of CHI 2022