  This paper tackles automatically discovering phone-like acoustic units (AUD)
from unlabeled speech data. Past studies usually proposed single-step
approaches. We propose a two-stage approach: the first stage learns a
subword-discriminative feature representation and the second stage applies
clustering to the learned representation and obtains phone-like clusters as the
discovered acoustic units. In the first stage, a recently proposed method in
the task of unsupervised subword modeling is improved by replacing a
monolingual out-of-domain (OOD) ASR system with a multilingual one to create a
subword-discriminative representation that is more language-independent. In the
second stage, segment-level k-means is adopted, and two methods to represent
the variable-length speech segments as fixed-dimension feature vectors are
compared. Experiments on a very low-resource Mboshi language corpus show that
our approach outperforms state-of-the-art AUD in both normalized mutual
information (NMI) and F-score. The multilingual ASR improved upon the
monolingual ASR in providing OOD phone labels and in estimating the phone
boundaries. A comparison of our systems with and without knowing the
ground-truth phone boundaries showed a 16% NMI performance gap, suggesting that
the current approach can significantly benefit from improved phone boundary
estimation.
