Age-specialized adapters for child ASR reduce overall WER from 12.6% to 12.3% and macro WER from 18.4% to 17.6% versus shared adapter baseline, with predicted-age routing nearly matching ground-truth.
Harnessing the Power of the Crowd to Increase Capacity for Data Science in the Social Sector
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We present three case studies of organizations using a data science competition to answer a pressing question. The first is in education where a nonprofit that creates smart school budgets wanted to automatically tag budget line items. The second is in public health, where a low-cost, nonprofit women's health care provider wanted to understand the effect of demographic and behavioral questions on predicting which services a woman would need. The third and final example is in government innovation: using online restaurant reviews from Yelp, competitors built models to forecast which restaurants were most likely to have hygiene violations when visited by health inspectors. Finally, we reflect on the unique benefits of the open, public competition model.
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Age-Aware Adapter Tuning for Children's Speech Recognition
Age-specialized adapters for child ASR reduce overall WER from 12.6% to 12.3% and macro WER from 18.4% to 17.6% versus shared adapter baseline, with predicted-age routing nearly matching ground-truth.