  This paper describes a novel knowledge distillation framework that leverages
acoustically qualified speech data included in an existing training data pool
as privileged information. In our proposed framework, a student network is
trained with multiple soft targets for each utterance that consist of main soft
targets from original speakers' utterance and alternative targets from other
speakers' utterances spoken under better acoustic conditions as a secondary
view. These qualified utterances from other speakers, used to generate better
soft targets, are collected from a qualified data pool by using strict
constraints in terms of word/phone/state durations. Our proposed method is a
form of target-side data augmentation that creates multiple copies of data with
corresponding better soft targets obtained from a qualified data pool. We show
in our experiments under acoustic model adaptation settings that the proposed
method, exploiting better soft targets obtained from various speakers, can
further improve recognition accuracy compared with conventional methods using
only soft targets from original speakers.
