Cross-Language Prominence Detection

We explore the ability to perform automatic prosodic analysis in one language using models trained on another. If we are successful, we should be able to identify prosodic elements in a language for which little or no prosodically labeled training data is available, using models trained on a language for which such training data exists. Given the laborious nature of manual prosodic annotation, such a process would vastly improve our ability to identify prosodic events in many languages and therefore to make use of such information in downstream processing tasks. The task we address here is the detection of intonational prominence, performing experiments using material from four languages: American English, Italian, French and German. While we do find that cross-language prominence detection is possible, we also find significant language-dependent differences. While we hypothesized that language family might serve as a reliable predictor of cross-language prosodic event detection accuracy, in our experiments this did not prove to be the case. Based upon our results, we suggest some directions that may be able to improve our cross-language approach.