RECOMMENDING MUSIC FOR LANGUAGE LEARNING: THE PROBLEM OF SINGING VOICE INTELLIGIBILITY

Abstract

Learning a new language is a complex task that requires time dedication and continuous learning. Language immersion is a recommended approach to maximize the learning by using the foreign language in daily activities. Research has shown that listening to music in a foreign language can enhance the listener's language and enrich his/her vocabulary. In this study, we investigate how to recommend songs that maximize the language learner benefit from listening to songs in the target languages. Specifically, we are proposing a method for annotating songs according to their intelligibility to human listeners. We then propose a number of acoustic features that measure the different factors affecting intelligibility in singing voice and use them in building a system to automatically estimate the intelligibility of a given song. Finally, we study the usability of crowdsourcing platforms for collecting and annotating songs according to their intelligibility score on a large scale.