  Speech style control and transfer techniques aim to enrich the diversity and
expressiveness of synthesized speech. Existing approaches model all speech
styles into one representation, lacking the ability to control a specific
speech feature independently. To address this issue, we introduce a novel
multi-reference structure to Tacotron and propose intercross training approach,
which together ensure that each sub-encoder of the multi-reference encoder
independently disentangles and controls a specific style. Experimental results
show that our model is able to control and transfer desired speech styles
individually.
