  Of all components of Prosody, Rhythm has been regarded as the hardest to
address, as it is utterly linked to Pitch and Intensity. Nevertheless, Rhythm
is a very good indicator of a speaker's fluency in a foreign language or even
of some diseases. Canonical ways to measure Rhythm, such as $\Delta C$ or
$\%V$, involve a cumbersome process of segment alignment, often leading to
modest and questionable results. Perceptively, however, rhythm does not sound
as difficult, as humans can grasp it even when the text is not fully
intelligible. In this work, we develop an empirical and unsupervised method of
rhythm assessment, which does not rely on the content. We have created a
fixed-length representation of each utterance, Peak Embedding (PE), which
codifies the proportional distance between peaks of the chosen Low-Level
Descriptors. Clustering pairs of small sentence-like units, we have attained
averages of 0.444 for Silhouette Coefficient using PE with Loudness, and 0.979
for Global Separability Index with a combination of PE with Pitch and Loudness.
Clustering same-structure words, we have attained averages of 0.196 for
Silhouette Coefficient and 0.864 for Global Separability Index for PE with
Loudness.
