  In settings where only unlabelled speech data is available, speech technology
needs to be developed without transcriptions, pronunciation dictionaries, or
language modelling text. A similar problem is faced when modelling infant
language acquisition. In these cases, categorical linguistic structure needs to
be discovered directly from speech audio. We present a novel unsupervised
Bayesian model that segments unlabelled speech and clusters the segments into
hypothesized word groupings. The result is a complete unsupervised tokenization
of the input speech in terms of discovered word types. In our approach, a
potential word segment (of arbitrary length) is embedded in a fixed-dimensional
acoustic vector space. The model, implemented as a Gibbs sampler, then builds a
whole-word acoustic model in this space while jointly performing segmentation.
We report word error rates in a small-vocabulary connected digit recognition
task by mapping the unsupervised decoded output to ground truth transcriptions.
The model achieves around 20% error rate, outperforming a previous HMM-based
system by about 10% absolute. Moreover, in contrast to the baseline, our model
does not require a pre-specified vocabulary size.
