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.
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 .
@cite_12
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In this paper, we consider regression problems with one-hidden-layer neural networks (1NNs). We distill some properties of activation functions that lead to @math in the neighborhood of the ground-truth parameters for the 1NN squared-loss objective. Most popular nonlinear activation functions satisfy the distilled properties, including rectified linear units (ReLUs), leaky ReLUs, squared ReLUs and sigmoids. For activation functions that are also smooth, we show @math guarantees of gradient descent under a resampling rule. For homogeneous activations, we show tensor methods are able to initialize the parameters to fall into the local strong convexity region. As a result, tensor initialization followed by gradient descent is guaranteed to recover the ground truth with sample complexity @math and computational complexity @math for smooth homogeneous activations with high probability, where @math is the dimension of the input, @math ( @math ) is the number of hidden nodes, @math is a conditioning property of the ground-truth parameter matrix between the input layer and the hidden layer, @math is the targeted precision and @math is the number of samples. To the best of our knowledge, this is the first work that provides recovery guarantees for 1NNs with both sample complexity and computational complexity @math in the input dimension and @math in the precision. 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). Abstract A method of speaker adaptation for continuous density hidden Markov models (HMMs) is presented. An initial speaker-independent system is adapted to improve the modelling of a new speaker by updating the HMM parameters. Statistics are gathered from the available adaptation data and used to calculate a linear regression-based transformation for the mean vectors. The transformation matrices are calculated to maximize the likelihood of the adaptation data and can be implemented using the forward–backward algorithm. By tying the transformations among a number of distributions, adaptation can be performed for distributions which are not represented in the training data. An important feature of the method is that arbitrary adaptation data can be used—no special enrolment sentences are needed. Experiments have been performed on the ARPA RM1 database using an HMM system with cross-word triphones and mixture Gaussian output distributions. Results show that adaptation can be performed using as little as 11 s of adaptation data, and that as more data is used the adaptation performance improves. For example, using 40 adaptation utterances, a 37 reduction in error from the speaker-independent system was achieved with supervised adaptation and a 32 reduction in unsupervised mode. Abstract This paper examines the application of linear transformations for speaker and environmental adaptation in an HMM-based speech recognition system. In particular, transformations that are trained in a maximum likelihood sense on adaptation data are investigated. Only model-based linear transforms are considered, since, for linear transforms, they subsume the appropriate feature–space transforms. The paper compares the two possible forms of model-based transforms: (i) unconstrained, where any combination of mean and variance transform may be used, and (ii) constrained, which requires the variance transform to have the same form as the mean transform. Re-estimation formulae for all appropriate cases of transform are given. This includes a new and efficient full variance transform and the extension of the constrained model–space transform from the simple diagonal case to the full or block–diagonal case. The constrained and unconstrained transforms are evaluated in terms of computational cost, recognition time efficiency, and use for speaker adaptive training. The recognition performance of the two model–space transforms on a large vocabulary speech recognition task using incremental adaptation is investigated. In addition, initial experiments using the constrained model–space transform for speaker adaptive training are detailed.