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We propose a multi-singer emotional singing voice synthesizer, Muse-SVS, thatexpresses emotion at various intensity levels by controlling subtle changes inpitch, energy, and phoneme duration while accurately following the score. Tocontrol multiple style attributes while avoiding loss of fidelity andexpressiveness due to interference between attributes, Muse-SVS represents allattributes and their relations together by a joint embedding in a unifiedembedding space. Muse-SVS can express emotional intensity levels not includedin the training data through embedding interpolation and extrapolation. We alsopropose a statistical pitch predictor to express pitch variance according toemotional intensity, and a context-aware residual duration predictor to preventthe accumulation of variances in phoneme duration, which is crucial forsynchronization with instrumental parts. In addition, we propose a novelASPP-Transformer, which combines atrous spatial pyramid pooling (ASPP) andTransformer, to improve fidelity and expressiveness by referring to broadcontexts. In experiments, Muse-SVS exhibited improved fidelity, expressiveness,and synchronization performance compared with baseline models. Thevisualization results show that Muse-SVS effectively express the variance inpitch, energy, and phoneme duration according to emotional intensity. To thebest of our knowledge, Muse-SVS is the first neural SVS capable of controllingemotional intensity.

MuSE-SVS: Multi-Singer Emotional Singing Voice Synthesizer that Controls Emotional Intensity