Katie Wetstone
Katie Wetstone is a data scientist with a passion for leveraging machine learning tools to promote sustainable, ethical, and just change. At DrivenData, she works to implement open-source machine learning competitions and direct consulting projects that support mission-driven organizations. Her projects have spanned a variety of issues including public health, conservation, and education. She holds a BA in chemistry from Harvard University, and a Masters of Development Practice from the University of California, Berkeley.
Session
Improving automatic speech recognition (ASR) for children is needed to enhance education and early childhood development. When ASR fails for children, reading assessments mis-score, speech therapy tools become unreliable, and many classroom tools cannot be built at all. The ASR gap exists because data sensitivity complicates the collection and sharing of transcribed child audio.
In this presentation, we’ll share how to unblock progress by creating public useful AI infrastructure even when data can’t be shared openly. We’ll discuss what makes child ASR so hard, how we advanced the field with an AI modeling competition, and best practices for sharing pretrained models.