  Unsupervised spoken term discovery (UTD) aims at finding recurring segments
of speech from a corpus of acoustic speech data. One potential approach to this
problem is to use dynamic time warping (DTW) to find well-aligning patterns
from the speech data. However, automatic selection of initial candidate
segments for the DTW-alignment and detection of "sufficiently good" alignments
among those require some type of pre-defined criteria, often operationalized as
threshold parameters for pair-wise distance metrics between signal
representations. In the existing UTD systems, the optimal hyperparameters may
differ across datasets, limiting their applicability to new corpora and truly
low-resource scenarios. In this paper, we propose a novel probabilistic
approach to DTW-based UTD named as PDTW. In PDTW, distributional
characteristics of the processed corpus are utilized for adaptive evaluation of
alignment quality, thereby enabling systematic discovery of pattern pairs that
have similarity what would be expected by coincidence. We test PDTW on Zero
Resource Speech Challenge 2017 datasets as a part of 2020 implementation of the
challenge. The results show that the system performs consistently on all five
tested languages using fixed hyperparameters, clearly outperforming the earlier
DTW-based system in terms of coverage of the detected patterns.
