  In text-to-speech synthesis, the ability to control voice characteristics is
vital for various applications. By leveraging thriving text prompt-based
generation techniques, it should be possible to enhance the nuanced control of
voice characteristics. While previous research has explored the prompt-based
manipulation of voice characteristics, most studies have used pre-recorded
speech, which limits the diversity of voice characteristics available. Thus, we
aim to address this gap by creating a novel corpus and developing a model for
prompt-based manipulation of voice characteristics in text-to-speech synthesis,
facilitating a broader range of voice characteristics. Specifically, we propose
a method to build a sizable corpus pairing voice characteristics descriptions
with corresponding speech samples. This involves automatically gathering
voice-related speech data from the Internet, ensuring its quality, and manually
annotating it using crowdsourcing. We implement this method with Japanese
language data and analyze the results to validate its effectiveness.
Subsequently, we propose a construction method of the model to retrieve speech
from voice characteristics descriptions based on a contrastive learning method.
We train the model using not only conservative contrastive learning but also
feature prediction learning to predict quantitative speech features
corresponding to voice characteristics. We evaluate the model performance via
experiments with the corpus we constructed above.
