Evaluating two versions of the momel pitch modelling algorithm on a corpus of read speech in Korean

The Momel algorithm provides an automatic factoring of raw fundamental frequency into two components: a microprosodic component, corresponding to local variations of pitch caused by the phonetic nature of the speech segments and a macroprosodic component corresponding to the overall pitch pattern of the utterance which is then represented as a sequence of pitch targets. An earlier evaluation estimated the overall efficiency of the algorithm (F-measure) at around 95% on a corpus of read speech for 5 European languages and at around 93% for a corpus of spontaneous speech. In this paper we present the results of the evaluation of the output of two versions of the Momel algorithm as compared with manually corrected pitch targets for a corpus of just over 2 hours of read speech in Korean (40 continuous 5-sentence passages, each read by 5 male and 5 female speakers). The results show that the new version of the Momel algorithm performs systematically better than the earlier version.