Accurate prosodic phrase prediction can improve
the naturalness of speech synthesis. Predicting the prosodic
phrase can be regarded as a sequence labeling problem and
the Conditional Random Field (CRF) is typically used to
solve it. Mongolian is an agglutinative language, in which
massive words can be formed by concatenating these stems
and suffixes. This character makes it difficult to build a
Mongolian prosodic phrase predictions system, based on
CRF, that has high performance. We introduce a new
method that segments Mongolian word into stem and suffix
as individual token. The proposed method integrates
multiple features according to the characteristics of
Mongolian word formation. We conduct the contrast
experiment by selecting the following features: word, multilevel Part-of-Speech (POS), multi-level lexical for suffix and
the existence for suffix. The experimental results show that
our method has significantly enhanced the performance of
the Mongolian prosodic phrase prediction system through
comparing with the conventional method that treats
Mongolian word as token directly. The word feature, level
one lexical for suffix feature and existence for suffix feature
are effective. The best result is measured by F1-measure as
82.49%