A Deep Learning Approach to Data-driven Parameterizations for Statistical Parametric Speech Synthesis
Table 2: ClusterGen synthesis voice build
['Voice', 'DNN Params', 'MCEP params']
The next set of tests were on using the deep neural network’s 50-dimensional encoding as a parameterization for the ClusterGen statistical parametric synthesizer. All of these tests were on the SLT voice. The first column describes the structure of the Stacked Denoising Autoencoder. The unwrapped Multi Layer Perceptron that corresponds to each SDA would be twice as deep. As can be seen in the table, for a constant depth, increasing the width of the layers of the neural network improves the performance of the network. Increasing the depth, however, makes a much bigger impact on performance. A deep narrow network also takes substantially less time to train compared to a shallow wide network. These trends are encouraging, and we expect further investigation in these directions to improve the performance further.