  Neural text-to-speech (TTS) can provide quality close to natural speech if an
adequate amount of high-quality speech material is available for training.
However, acquiring speech data for TTS training is costly and time-consuming,
especially if the goal is to generate different speaking styles. In this work,
we show that we can transfer speaking style across speakers and improve the
quality of synthetic speech by training a multi-speaker multi-style (MSMS)
model with long-form recordings, in addition to regular TTS recordings. In
particular, we show that 1) multi-speaker modeling improves the overall TTS
quality, 2) the proposed MSMS approach outperforms pre-training and fine-tuning
approach when utilizing additional multi-speaker data, and 3) long-form
speaking style is highly rated regardless of the target text domain.
