  This paper presents a novel approach for enhancing the multiple sets of
acoustic patterns automatically discovered from a given corpus. In a previous
work it was proposed that different HMM configurations (number of states per
model, number of distinct models) for the acoustic patterns form a
two-dimensional space. Multiple sets of acoustic patterns automatically
discovered with the HMM configurations properly located on different points
over this two-dimensional space were shown to be complementary to one another,
jointly capturing the characteristics of the given corpus. By representing the
given corpus as sequences of acoustic patterns on different HMM sets, the
pattern indices in these sequences can be relabeled considering the context
consistency across the different sequences. Good improvements were observed in
preliminary experiments of pattern spoken term detection (STD) performed on
both TIMIT and Mandarin Broadcast News with such enhanced patterns.
