  State-of-the-art Automatic Speech Recognition (ASR) systems are known to
exhibit disparate performance on varying speech accents. To improve performance
on a specific target accent, a commonly adopted solution is to finetune the ASR
model using accent-specific labeled speech. However, acquiring large amounts of
labeled speech for specific target accents is challenging. Choosing an
informative subset of speech samples that are most representative of the target
accents becomes important for effective ASR finetuning. To address this
problem, we propose DITTO (Data-efficient and faIr Targeted subseT selectiOn)
that uses Submodular Mutual Information (SMI) functions as acquisition
functions to find the most informative set of utterances matching a target
accent within a fixed budget. An important feature of DITTO is that it supports
fair targeting for multiple accents, i.e. it can automatically select
representative data points from multiple accents when the ASR model needs to
perform well on more than one accent. We show that DITTO is 3-5 times more
label-efficient than other speech selection methods on the IndicTTS and L2
datasets.
