  Recently, deep learning-based Text-to-Speech (TTS) systems have achieved
high-quality speech synthesis results. Recurrent neural networks have become a
standard modeling technique for sequential data in TTS systems and are widely
used. However, training a TTS model which includes RNN components requires
powerful GPU performance and takes a long time. In contrast, CNN-based sequence
synthesis techniques can significantly reduce the parameters and training time
of a TTS model while guaranteeing a certain performance due to their high
parallelism, which alleviate these economic costs of training. In this paper,
we propose a lightweight TTS system based on deep convolutional neural
networks, which is a two-stage training end-to-end TTS model and does not
employ any recurrent units. Our model consists of two stages: Text2Spectrum and
SSRN. The former is used to encode phonemes into a coarse mel spectrogram and
the latter is used to synthesize the complete spectrum from the coarse mel
spectrogram. Meanwhile, we improve the robustness of our model by a series of
data augmentations, such as noise suppression, time warping, frequency masking
and time masking, for solving the low resource mongolian problem. Experiments
show that our model can reduce the training time and parameters while ensuring
the quality and naturalness of the synthesized speech compared to using
mainstream TTS models. Our method uses NCMMSC2022-MTTSC Challenge dataset for
validation, which significantly reduces training time while maintaining a
certain accuracy.
