This paper proposes a novel noise compensation algorithm for a glottal excitation model in a deep learning (DL)-based speech synthesis system.
  To generate high-quality speech synthesis outputs, the balance between harmonic and noise components of the glottal excitation signal should be well-represented by the DL network.
  However, it is hard to accurately model the noise component because the DL training process inevitably results in statistically smoothed outputs; thus, it is essential to introduce an additional noise compensation process.
  We propose a modeling-by-generation structure-based noise compensation method that the missing noise component in the generated glottal signal is directly extracted and parameterized during the entire training process.
  By modeling the noise component using the additional DL network, the proposed system successfully compensates the missing noise component.
  Objective and subjective test results confirm that the synthesized speech with the proposed noise compensation method is superior to that with conventional methods.