Labeling of Symbolic Prosody Breaks for the Slovenian Language

Abstract

This paper presents the data-driven prediction of word level prosody breaks modelling for the Slovenian language. Automatic learning techniques depend on the construction of a large corpus labeled appropriately. This labeling can be done either automatically, or by hand. While automatic labeling can be less accurate than hand labeling, the latter is very time consuming and, in some cases, inconsistent. Therefore, a new interactive tool for word level prosody labeling (major/minor breaks) is presented together with a new semi-automatic approach for determining prosody breaks. This interactive tool combines the advantages of hand labeling and automatic labeling by achieving a high consistency in labeling and reducing the time needed for hand labeling. The labeled Slovenian corpus has been used to train our phrase break prediction module, implementing a neural network (NN) structure. Experiments for the data-driven prediction of major = minor and major/minor phrase breaks were performed. The prediction accuracy achieved marks state-of-the-art word level prosody breaks prediction for the Slovenian language and is comparable to the prediction accuracy of other approaches in which more complex NN structures (Müller et al., 2000) or other prediction methods (Black and Tailor, 1997) were applied, and a much larger corpus was used for training. The overall prediction accuracy achieved is 94% for major = minor breaks and over 98/92% for major/minor phrase breaks, respectively.