  This research addresses the problem of acoustic modeling of low-resource
languages for which transcribed training data is absent. The goal is to learn
robust frame-level feature representations that can be used to identify and
distinguish subword-level speech units. The proposed feature representations
comprise various types of multilingual bottleneck features (BNFs) that are
obtained via multi-task learning of deep neural networks (MTL-DNN). One of the
key problems is how to acquire high-quality frame labels for untranscribed
training data to facilitate supervised DNN training. It is shown that learning
of robust BNF representations can be achieved by effectively leveraging
transcribed speech data and well-trained automatic speech recognition (ASR)
systems from one or more out-of-domain (resource-rich) languages. Out-of-domain
ASR systems can be applied to perform speaker adaptation with untranscribed
training data of the target language, and to decode the training speech into
frame-level labels for DNN training. It is also found that better frame labels
can be generated by considering temporal dependency in speech when performing
frame clustering. The proposed methods of feature learning are evaluated on the
standard task of unsupervised subword modeling in Track 1 of the ZeroSpeech
2017 Challenge. The best performance achieved by our system is $9.7\%$ in terms
of across-speaker triphone minimal-pair ABX error rate, which is comparable to
the best systems reported recently. Lastly, our investigation reveals that the
closeness between target languages and out-of-domain languages and the amount
of available training data for individual target languages could have
significant impact on the goodness of learned features.
