  Unsupervised segmentation and clustering of unlabelled speech are core
problems in zero-resource speech processing. Most approaches lie at
methodological extremes: some use probabilistic Bayesian models with
convergence guarantees, while others opt for more efficient heuristic
techniques. Despite competitive performance in previous work, the full Bayesian
approach is difficult to scale to large speech corpora. We introduce an
approximation to a recent Bayesian model that still has a clear objective
function but improves efficiency by using hard clustering and segmentation
rather than full Bayesian inference. Like its Bayesian counterpart, this
embedded segmental K-means model (ES-KMeans) represents arbitrary-length word
segments as fixed-dimensional acoustic word embeddings. We first compare
ES-KMeans to previous approaches on common English and Xitsonga data sets (5
and 2.5 hours of speech): ES-KMeans outperforms a leading heuristic method in
word segmentation, giving similar scores to the Bayesian model while being 5
times faster with fewer hyperparameters. However, its clusters are less pure
than those of the other models. We then show that ES-KMeans scales to larger
corpora by applying it to the 5 languages of the Zero Resource Speech Challenge
2017 (up to 45 hours), where it performs competitively compared to the
challenge baseline.
