Scaling and bias codes for modeling speaker-adaptive DNN-based speech
Most neural-network based speaker-adaptive acoustic models for speech
synthesis can be categorized into either layer-based or input-code approaches.
Although both approaches have their own pros and cons, most existing works on
speaker adaptation focus on improving one or the other. In this paper, after we
first systematically overview the common principles of neural-network based
speaker-adaptive models, we show that these approaches can be represented in a
unified framework and can be generalized further. More specifically, we
introduce the use of scaling and bias codes as generalized means for
speaker-adaptive transformation. By utilizing these codes, we can create a more
efficient factorized speaker-adaptive model and capture advantages of both
approaches while reducing their disadvantages. The experiments show that the
proposed method can improve the performance of speaker adaptation compared with
speaker adaptation based on the conventional input code.