  Many neural text-to-speech architectures can synthesize nearly natural speech
from text inputs. These architectures must be trained with tens of hours of
annotated and high-quality speech data. Compiling such large databases for
every new voice requires a lot of time and effort. In this paper, we describe a
method to extend the popular Tacotron-2 architecture and its training with data
augmentation to enable single-speaker synthesis using a limited amount of
specific training data. In contrast to elaborate augmentation methods proposed
in the literature, we use simple stationary noises for data augmentation. Our
extension is easy to implement and adds almost no computational overhead during
training and inference. Using only two hours of training data, our approach was
rated by human listeners to be on par with the baseline Tacotron-2 trained with
23.5 hours of LJSpeech data. In addition, we tested our model with a
semantically unpredictable sentences test, which showed that both models
exhibit similar intelligibility levels.
