text: 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.
  @cite_12: This paper proposes a simple yet effective model-based neural network speaker adaptation technique that learns speaker-specific hidden unit contributions given adaptation data, without requiring any form of speaker-adaptive training, or labelled adaptation data. An additional amplitude parameter is defined for each hidden unit; the amplitude parameters are tied for each speaker, and are learned using unsupervised adaptation. We conducted experiments on the TED talks data, as used in the International Workshop on Spoken Language Translation (IWSLT) evaluations. Our results indicate that the approach can reduce word error rates on standard IWSLT test sets by about 8–15 relative compared to unadapted systems, with a further reduction of 4–6 relative when combined with feature-space maximum likelihood linear regression (fMLLR). The approach can be employed in most existing feed-forward neural network architectures, and we report results using various hidden unit activation functions: sigmoid, maxout, and rectifying linear units (ReLU).
summary: Learning Hidden Unit Contribution (LHUC) @cite_12 is an adaptation method that transforms outputs of the activation function using a speaker-dependent diagonal transformation matrix, which significantly reduces the number of parameters: where @math is a diagonal matrix for speaker @math , @math is an operation to extract diagonal elements of a @math matrix as a @math vector, and @math is an element-wise multiplication of vectors. In LHUC, since we apply the transformation after the activation function of the current layer, we may write the LHUC operation at the next hidden layer as follows: From these equations, we see that a speaker-specific weight matrix @math is factorized as @math .