text: 364,136,1446136483589537796,349321075,Oncel Tuzel,Style equalization is a new paper from our research team @Apple to train controllable generative sequence models that can mimic speech or handwriting styles using a single reference example. Paper: <LINK> Synthesis videos: <LINK> R. Chang et al. <LINK>,https://arxiv.org/abs/2110.02891,"Controllable generative sequence models with the capability to extract and replicate the style of specific examples enable many applications, including narrating audiobooks in different voices, auto-completing and auto-correcting written handwriting, and generating missing training samples for downstream recognition tasks. However, typical training algorithms for these controllable sequence generative models suffer from the training-inference mismatch, where the same sample is used as content and style input during training but different samples are given during inference. In this paper, we tackle the training-inference mismatch encountered during unsupervised learning of controllable generative sequence models. By introducing a style transformation module that we call style equalization, we enable training using different content and style samples and thereby mitigate the training-inference mismatch. To demonstrate its generality, we applied style equalization to text-to-speech and text-to-handwriting synthesis on three datasets. Our models achieve state-of-the-art style replication with a similar mean style opinion score as the real data. Moreover, the proposed method enables style interpolation between sequences and generates novel styles. ","Style Equalization: Unsupervised Learning of Controllable Generative