  This study addresses the problem of unsupervised subword unit discovery from
untranscribed speech. It forms the basis of the ultimate goal of ZeroSpeech
2019, building text-to-speech systems without text labels. In this work, unit
discovery is formulated as a pipeline of phonetically discriminative feature
learning and unit inference. One major difficulty in robust unsupervised
feature learning is dealing with speaker variation. Here the robustness towards
speaker variation is achieved by applying adversarial training and FHVAE based
disentangled speech representation learning. A comparison of the two approaches
as well as their combination is studied in a DNN-bottleneck feature (DNN-BNF)
architecture. Experiments are conducted on ZeroSpeech 2019 and 2017.
Experimental results on ZeroSpeech 2017 show that both approaches are effective
while the latter is more prominent, and that their combination brings further
marginal improvement in across-speaker condition. Results on ZeroSpeech 2019
show that in the ABX discriminability task, our approaches significantly
outperform the official baseline, and are competitive to or even outperform the
official topline. The proposed unit sequence smoothing algorithm improves
synthesis quality, at a cost of slight decrease in ABX discriminability.
