Opportunities and Challenges of Automatic Speech Recognition Systems for Low-Resource Language Speakers
Opportunities and Challenges of Automatic Speech Recognition Systems for Low-Resource Language Speakers
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DOI：
10.1145/3491102.3517639
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
Thomas Reitmaier;E. Wallington;D. Raju;Ondrej Klejch;Jennifer Pearson;Matt Jones;P. Bell;Simon Robinson
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文献类型：
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作者：
Thomas Reitmaier;E. Wallington;D. Raju;Ondrej Klejch;Jennifer Pearson;Matt Jones;P. Bell;Simon Robinson
自动语音识别(ASR)的研究人员正将他们的注意力转向支持低资源语言，如isiXhosa或马拉提语，而培训资源有限。我们报告并反思了ASR和HCI之间的合作研究，以定位ASR支持的技术，以满足南非开普敦郊区和印度孟买两个低资源语言使用者社区的需求和功能。我们建立在长期的社区伙伴关系基础上，并利用语言学、媒体研究和人机界面学术来指导我们的研究。我们演示了多种设计方法：远程参与参与者；收集语音数据以测试ASR模型；并最终与用户现场测试模型。经过研究，我们确定了ASR的机遇、挑战和使用案例，特别是支持WhatsApp语音消息的普遍使用。最后，我们揭示了对ASR和HCI之间的协作的影响，这些协作推动了CHI围绕数据、伦理和人工智能的重要讨论。
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.