Singing speaker clustering based on subspace learning in the GMM mean supervector space

► Mixed style speech causes problems when training acoustic models for speech applications, such as speaker ID and ASR. ► This study is a first attempt for speaker clustering under mixed speaking styles which include reading and singing. ► Two types of subspace learning strategies in the GMM mean supervector space are studied: unsupervised and supervised. ► Advanced clustering algorithms are evaluated on a database that includes reading and singing the lyrics for each speaker. ► LPP subspace learning and a proposed cluster refining based on PLDA significantly improves clustering accuracies.