Investigating data partitioning strategies for crosslinguistic low-resource ASR evaluation. (arXiv:2208.12888v1 [cs.CL])

Many automatic speech recognition (ASR) data sets include a single
pre-defined test set consisting of one or more speakers whose speech never
appears in the training set. This "hold-speaker(s)-out" data partitioning
strategy, however, may not be ideal for data sets in which the number of
speakers is very small. This study investigates ten different data split
methods for five languages with minimal ASR training resources. We find that
(1) model performance varies greatly depending on which speaker is selected for …

arxiv asr data data partitioning evaluation partitioning strategies