A Comparative Analysis of Pretrained Language Models for Text-to-Speech
Marcel Granero-Moya Penny Karanasou Sri Karlapati Bastian Schnell Nicole Peinelt Alexis Moinet Thomas Drugman
State-of-the-art text-to-speech (TTS) systems have utilized pretrained
language models (PLMs) to enhance prosody and create more natural-sounding
speech. However, while PLMs have been extensively researched for natural
language understanding (NLU), their impact on TTS has been overlooked. In this
study, we aim to address this gap by conducting a comparative analysis of
different PLMs for two TTS tasks: prosody prediction and pause prediction.
Firstly, we trained a prosody prediction model using 15 different PLMs. Our
findings revealed a logarithmic relationship between model size and quality, as
well as significant performance differences between neutral and expressive
prosody. Secondly, we employed PLMs for pause prediction and found that the
task was less sensitive to small models. We also identified a strong
correlation between our empirical results and the GLUE scores obtained for
these language models. To the best of our knowledge, this is the first study of
its kind to investigate the impact of different PLMs on TTS.