Experiments on automatic prosodic labeling

Abstract

This paper presents results from experiments on automatic prosodic labeling. Using the WEKA machine learning software [1], classifiers were trained to determine for each syllable in a speech database of a male speaker its pitch accent and its boundary tone. Pitch accents and boundaries are according to the GToBI(S) dialect, with slight modifications. Classification was based on 35 attributes involving PaIntE F0 parametrization [2] and normalized phone durations, but also some phonological information as well as higher-linguistic information. Several classification algorithms yield results of approx. 78% accuracy on the word level for pitch accents, and approx. 88% accuracy on the word level for phrase boundaries, which compare very well to results of other studies. The classifiers generalize to similar data of a female speaker in that they perform equally well as classifiers trained directly on the female data. Index Terms: perception of prosody, prosodic labeling, F0 parametrization