Alexa researchers improve AI error rate up to 30% by reducing data imbalance

Imbalanced training data is a major hurdle for classifiers that is, machine learning systems which sort inputs into classes. (Think object-detecting security cameras and smart speakers that distinguish among speakers.) When one category of samples disproportionately contributes to a corpus, the classifier naturally encounters it more often than others, and so runs the risk of becoming biased toward it. Researchers at Amazon’s Alexa division, though, say they’ve developed a technique that can reduce error rates in some data-imbalanced systems by up to 30 percent. They describe it in a recently published paper (“Deep Embeddings for Rare Audio Event Detection with Imbalanced Data”) scheduled to be present at the International Conference on Acoustics, Speech, and Signal Processing in Brighton this spring.