Estimating classifier performance in unknown noise
Ehsan Variani, Hynek Heřmanský · 2012
We propose and investigate a non-parametric method for identifying regions of speech that have unexpected distortions not seen in the training data. The method does not require knowledge of correct labels and relies only on divergence between statistics of the test and training data. Our experiments show that the proposed method re-quires a relatively small amount of test data of the order of several seconds to stabilize, and correlates well with recognition error observed on the test data. Index Terms: Unexpected distortions, confidence esti-mation, machine recognition of speech