Singing speaker clustering based on subspace learning in the GMM mean supervector space
Despite a long-standing effort to characterize various aspects of the singing voice and their relations to speech, the lack of a suitable and publicly available dataset has precluded any systematic study on the quantitative difference between singing and speech at the phone level. We hereby present the NUS Sung and Spoken Lyrics Corpus (NUS-48E corpus) as the first step toward a large, phonetically annotated corpus for singing voice research. The corpus is a 169-min collection of audio recordings of the sung and spoken lyrics of 48 (20 unique) English songs by 12 subjects and a complete set of transcriptions and duration annotations at the phone level for all recordings of sung lyrics, comprising 25,474 phone instances. Using the NUS-48E corpus, we conducted a preliminary, quantitative study on the comparison between singing voice and speech. The study includes duration analyses of the sung and spoken lyrics, with a primary focus on the behavior of consonants, and experiments aiming to gauge how acoustic representations of spoken and sung phonemes differ, as well as how duration and pitch variations may affect the Mel Frequency Cepstral Coefficients (MFCC) features.