Combining syntactic and acoustic features for prosodic boundary detection in Russian

Abstract

This paper presents a two-step method of automatic prosodic boundary detection using both textual and acoustic features. Firstly, we predict possible boundary positions using textual features; secondly, we detect the actual boundaries at the predicted positions using acoustic features. For evaluation of the algorithms we use a 26-h subcorpus of CORPRES, a prosodically annotated corpus of Russian read speech. We have also conducted two independent experiments using acoustic features and textual features separately. Acoustic features alone enable to achieve the F1 measure of 0.85, precision of 0.94, recall of 0.78. Textual features alone work with the F1 measure of 0.84, precision of 0.84, recall of 0.83. The proposed two-step approach combining the two groups of features yields the efficiency of 0.90, recall of 0.85 and precision of 0.99. It preserves the high recall provided by textual information and the high precision achieved using acoustic information. This is the best published result for Russian.