  How can we effectively develop speech technology for languages where no
transcribed data is available? Many existing approaches use no annotated
resources at all, yet it makes sense to leverage information from large
annotated corpora in other languages, for example in the form of multilingual
bottleneck features (BNFs) obtained from a supervised speech recognition
system. In this work, we evaluate the benefits of BNFs for subword modeling
(feature extraction) in six unseen languages on a word discrimination task.
First we establish a strong unsupervised baseline by combining two existing
methods: vocal tract length normalisation (VTLN) and the correspondence
autoencoder (cAE). We then show that BNFs trained on a single language already
beat this baseline; including up to 10 languages results in additional
improvements which cannot be matched by just adding more data from a single
language. Finally, we show that the cAE can improve further on the BNFs if
high-quality same-word pairs are available.
