PauseSpeech: Natural Speech Synthesis via Pre-trained Language Model and Pause-Based Prosody Modeling

Ji Sang Hwang, Sang Hoon Lee, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Although text-to-speech (TTS) systems have significantly improved, most TTS systems still have limitations in synthesizing speech with appropriate phrasing. For natural speech synthesis, it is important to synthesize the speech with a phrasing structure that groups words into phrases based on semantic information. In this paper, we propose PuaseSpeech, a speech synthesis system with a pre-trained language model and pause-based prosody modeling. First, we introduce a phrasing structure encoder that utilizes a context representation from the pre-trained language model. In the phrasing structure encoder, we extract a speaker-dependent syntactic representation from the context representation and then predict a pause sequence that separates the input text into phrases. Furthermore, we introduce a pause-based word encoder to model word-level prosody based on pause sequence. Experimental results show PauseSpeech outperforms previous models in terms of naturalness. Furthermore, in terms of objective evaluations, we can observe that our proposed methods help the model decrease the distance between ground-truth and synthesized speech. Audio samples are available at

Original languageEnglish
Title of host publicationPattern Recognition - 7th Asian Conference, ACPR 2023, Proceedings
EditorsHuimin Lu, Michael Blumenstein, Sung-Bae Cho, Cheng-Lin Liu, Yasushi Yagi, Tohru Kamiya
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783031476334
Publication statusPublished - 2023
Event7th Asian Conference on Pattern Recognition, ACPR 2023 - Kitakyushu, Japan
Duration: 2023 Nov 52023 Nov 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14406 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th Asian Conference on Pattern Recognition, ACPR 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • Pause-based prosody modeling
  • Pre-trained language model
  • Text-to-speech

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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