SinTechSVS: A Singing Technique Controllable Singing Voice Synthesis System

The precise control of singing techniques is of utmost importance in achieving emotionally expressive vocal performances. To bridge the gap between current Singing Voice Synthesis (SVS) systems and human singers, our paper focuses on developing an SVS system that allows for control over singing techniques. In this paper, we introduce SinTechSVS, a singing technique controllable SVS system composed of a singing technique annotator, a singing technique controllable synthesizer, and a singing technique recommender. Our approach leverages transfer learning for efficient singing technique annotation and adapts the DiffSinger framework with additional style encoders and an attention-based singing technique local score (STLS) module to enhance singing technique controllability. We also propose a Seq2Seq singing technique recommender for the new task of Singing Technique Recommendation (STR). Experimental results demonstrate that SinTechSVS significantly improves the quality and expressiveness of synthesized vocal performances, with comparable general synthesis capabilities to state-of-the-art SVS systems and enhanced control over singing techniques, as evidenced by objective and subjective evaluations. To the best of our knowledge, SinTechSVS is the first SVS capable of controlling singing techniques.