  Currently, there are increasing interests in text-to-speech (TTS) synthesis
to use sequence-to-sequence models with attention. These models are end-to-end
meaning that they learn both co-articulation and duration properties directly
from text and speech. Since these models are entirely data-driven, they need
large amounts of data to generate synthetic speech with good quality. However,
in challenging speaking styles, such as Lombard speech, it is difficult to
record sufficiently large speech corpora. Therefore, in this study we propose a
transfer learning method to adapt a sequence-to-sequence based TTS system of
normal speaking style to Lombard style. Moreover, we experiment with a WaveNet
vocoder in synthesis of Lombard speech. We conducted subjective evaluations to
assess the performance of the adapted TTS systems. The subjective evaluation
results indicated that an adaptation system with the WaveNet vocoder clearly
outperformed the conventional deep neural network based TTS system in synthesis
of Lombard speech.
