  We investigate segmenting and clustering speech into low-bitrate phone-like
sequences without supervision. We specifically constrain pretrained
self-supervised vector-quantized (VQ) neural networks so that blocks of
contiguous feature vectors are assigned to the same code, thereby giving a
variable-rate segmentation of the speech into discrete units. Two segmentation
methods are considered. In the first, features are greedily merged until a
prespecified number of segments are reached. The second uses dynamic
programming to optimize a squared error with a penalty term to encourage fewer
but longer segments. We show that these VQ segmentation methods can be used
without alteration across a wide range of tasks: unsupervised phone
segmentation, ABX phone discrimination, same-different word discrimination, and
as inputs to a symbolic word segmentation algorithm. The penalized method
generally performs best. While results are only comparable to the
state-of-the-art in some cases, in all tasks a reasonable competing approach is
outperformed at a substantially lower bitrate.
