In this episode, part 4 in our Black in AI series, i’m joined by Charles Onu, Phd Student at McGill University in Montreal & Founder of Ubenwa, a startup tackling the problem of infant mortality due to asphyxia.
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Using SVMs and other techniques from the field of automatic speech recognition, Charles and his team have built a model that detects asphyxia based on the audible noises the child makes upon birth. We go into the process he used to collect his training data, including the specific methods they used to record samples, and how their samples will be used to maximize accuracy in the field. We also take a deep dive into some of the challenges of building and deploying the platform and mobile application. This is a really interesting use case, which I think you’ll enjoy.
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Mentioned in the Interview
- Fisher Foundation Africa
- Harnessing Infant Cry for swift, cost-effective Diagnosis of Perinatal
Asphyxia in low-resource settings
- Markov Chain Model
- Black in AI
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“More On That Later” by Lee Rosevere licensed under CC By 4.0