  In this work, we propose a hierarchical subspace model for acoustic unit
discovery. In this approach, we frame the task as one of learning embeddings on
a low-dimensional phonetic subspace, and simultaneously specify the subspace
itself as an embedding on a hyper-subspace. We train the hyper-subspace on a
set of transcribed languages and transfer it to the target language. In the
target language, we infer both the language and unit embeddings in an
unsupervised manner, and in so doing, we simultaneously learn a subspace of
units specific to that language and the units that dwell on it. We conduct our
experiments on TIMIT and two low-resource languages: Mboshi and Yoruba. Results
show that our model outperforms major acoustic unit discovery techniques, both
in terms of clustering quality and segmentation accuracy.
