  Speech evaluation is an essential component in computer-assisted language
learning (CALL). While speech evaluation on English has been popular, automatic
speech scoring on low resource languages remains challenging. Work in this area
has focused on monolingual specific designs and handcrafted features stemming
from resource-rich languages like English. Such approaches are often difficult
to generalize to other languages, especially if we also want to consider
suprasegmental qualities such as rhythm. In this work, we examine three
different languages that possess distinct rhythm patterns: English
(stress-timed), Malay (syllable-timed), and Tamil (mora-timed). We exploit
robust feature representations inspired by music processing and vector
representation learning. Empirical validations show consistent gains for all
three languages when predicting pronunciation, rhythm and intonation
performance.
