Cycle-consistent adversarial networks for non-parallel vocal effort based speaking style conversion

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • Tampere University
  • Research Organization of Information and Systems National Institute of Informatics
  • University of Edinburgh

Abstract

Speaking style conversion (SSC) is the technology of converting natural speech signals from one style to another. In this study, we propose the use of cycle-consistent adversarial networks (CycleGANs) for converting styles with varying vocal effort, and focus on conversion between normal and Lombard styles as a case study of this problem. We propose a parametric approach that uses the Pulse Model in Log domain (PML) vocoder to extract speech features. These features are mapped using the CycleGAN from utterances in the source style to the corresponding features of target speech. Finally, the mapped features are converted to a Lombard speech waveform with the PML. The CycleGAN was compared in subjective listening tests with 2 other standard mapping methods used in conversion, and the CycleGAN was found to have the best performance in terms of speech quality and in terms of the magnitude of the perceptual change between the two styles.

Details

Original languageEnglish
Title of host publication ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publication statusPublished - 1 May 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, United Kingdom
Duration: 12 May 201917 May 2019
Conference number: 44

Publication series

NameProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP
CountryUnited Kingdom
CityBrighton
Period12/05/201917/05/2019

Download statistics

No data available

ID: 32097934