DPP-TTS: Diversifying prosodic features of speech via determinantal
  point processes
Seongho Joo Hyukhun Koh Kyomin Jung
With the rapid advancement in deep generative models, recent neural
Text-To-Speech(TTS) models have succeeded in synthesizing human-like speech.
There have been some efforts to generate speech with various prosody beyond
monotonous prosody patterns. However, previous works have several limitations.
First, typical TTS models depend on the scaled sampling temperature for
boosting the diversity of prosody. Speech samples generated at high sampling
temperatures often lack perceptual prosodic diversity, which can adversely
affect the naturalness of the speech. Second, the diversity among samples is
neglected since the sampling procedure often focuses on a single speech sample
rather than multiple ones. In this paper, we propose DPP-TTS: a text-to-speech
model based on Determinantal Point Processes (DPPs) with a prosody diversifying
module. Our TTS model is capable of generating speech samples that
simultaneously consider perceptual diversity in each sample and among multiple
samples. We demonstrate that DPP-TTS generates speech samples with more
diversified prosody than baselines in the side-by-side comparison test
considering the naturalness of speech at the same time.