  Recent deep learning Text-to-Speech (TTS) systems have achieved impressive
performance by generating speech close to human parity. However, they suffer
from training stability issues as well as incorrect alignment of the
intermediate acoustic representation with the input text sequence. In this
work, we introduce Regotron, a regularized version of Tacotron2 which aims to
alleviate the training issues and at the same time produce monotonic
alignments. Our method augments the vanilla Tacotron2 objective function with
an additional term, which penalizes non-monotonic alignments in the
location-sensitive attention mechanism. By properly adjusting this
regularization term we show that the loss curves become smoother, and at the
same time Regotron consistently produces monotonic alignments in unseen
examples even at an early stage (13\% of the total number of epochs) of its
training process, whereas the fully converged Tacotron2 fails to do so.
Moreover, our proposed regularization method has no additional computational
overhead, while reducing common TTS mistakes and achieving slighlty improved
speech naturalness according to subjective mean opinion scores (MOS) collected
from 50 evaluators.
