Language identification for singing

In spoken language processing, considerable research has been accomplished on language identification. Singing language identification is an important yet challenging area that has attracted only a few researchers in music processing. As one information source that can be extracted from music, the language of vocal music is useful for song classification, recognition, and retrieval based on the singing language, specially in unlabeled or mislabeled music collections. In addition, consideration of singing as a speaking style introduces new challenges to existing language identification systems. Our objective in this paper, as one of the first attempts for singing language identification, is to evaluate successful LID systems, specifically PPRLM with singing speech. Further more, we propose a prosodic approach based on pitch contour approximation and compare the results to PPRLM system. Language identification performance for singing and read speech are compared in both systems. Finally, we combine the PPRLM and prosodic systems which achieves an average performance improvement of 4.7% for singing, and 8.7% for read speech compared to the baseline PPRLM system. Our evaluations are based on a multilingual singing corpus that we have collected for this study.