  Spoken term discovery from untranscribed speech audio could be achieved via a
two-stage process. In the first stage, the unlabelled speech is decoded into a
sequence of subword units that are learned and modelled in an unsupervised
manner. In the second stage, partial sequence matching and clustering are
performed on the decoded subword sequences, resulting in a set of discovered
words or phrases. A limitation of this approach is that the results of subword
decoding could be erroneous, and the errors would impact the subsequent steps.
While Siamese/Triplet network is one approach to learn segment representations
that can improve the discovery process, the challenge in spoken term discovery
under a complete unsupervised scenario is that training examples are
unavailable. In this paper, we propose to generate training examples from
initial hypothesized sequence clusters. The Siamese/Triplet network is trained
on the hypothesized examples to measure the similarity between two speech
segments and hereby perform re-clustering of all hypothesized subword sequences
to achieve spoken term discovery. Experimental results show that the proposed
approach is effective in obtaining training examples for Siamese and Triplet
networks, improving the efficacy of spoken term discovery as compared with the
original two-stage method.
