  Zero-resource speech technology is a growing research area that aims to
develop methods for speech processing in the absence of transcriptions,
lexicons, or language modelling text. Early term discovery systems focused on
identifying isolated recurring patterns in a corpus, while more recent
full-coverage systems attempt to completely segment and cluster the audio into
word-like units---effectively performing unsupervised speech recognition. This
article presents the first attempt we are aware of to apply such a system to
large-vocabulary multi-speaker data. Our system uses a Bayesian modelling
framework with segmental word representations: each word segment is represented
as a fixed-dimensional acoustic embedding obtained by mapping the sequence of
feature frames to a single embedding vector. We compare our system on English
and Xitsonga datasets to state-of-the-art baselines, using a variety of
measures including word error rate (obtained by mapping the unsupervised output
to ground truth transcriptions). Very high word error rates are reported---in
the order of 70--80% for speaker-dependent and 80--95% for speaker-independent
systems---highlighting the difficulty of this task. Nevertheless, in terms of
cluster quality and word segmentation metrics, we show that by imposing a
consistent top-down segmentation while also using bottom-up knowledge from
detected syllable boundaries, both single-speaker and multi-speaker versions of
our system outperform a purely bottom-up single-speaker syllable-based
approach. We also show that the discovered clusters can be made less speaker-
and gender-specific by using an unsupervised autoencoder-like feature extractor
to learn better frame-level features (prior to embedding). Our system's
discovered clusters are still less pure than those of unsupervised term
discovery systems, but provide far greater coverage.
