Performance Evaluation of Speaker-Identification Systems for Singing Voice Data

Automatic speaker-identification (SID) has long been an important research topic. It is aimed at identifying who among a set of enrolled persons spoke a given utterance. This study extends the conventional SID problem to examining if an SID system trained using speech data can identify the singing voices of the enrolled persons. Our experiment found that a standard SID system fails to identify most singing data, due to the significant differences between singing and speaking for a majority of people. In order for an SID system to handle both speech and singing data, we examine the feasibility of using model-adaptation strategy to enhance the generalization of a standard SID. Our experiments show that a majority of the singing clips can be correctly identified after adapting speech-derived voice models with some singing data.