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

No Thumbnail Available

Access rights

openAccess

URL

Journal Title

Journal ISSN

Volume Title

A4 Artikkeli konferenssijulkaisussa

Date

2019-05-01

Major/Subject

Mcode

Degree programme

Language

en

Pages

6835 - 6839

Series

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing

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.

Description

Keywords

Other note

Citation

Seshadri, S, Juvela, L, Yamagishi, J, Räsänen, O & Alku, P 2019, Cycle-consistent adversarial networks for non-parallel vocal effort based speaking style conversion . in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ., 8682648, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, pp. 6835 - 6839, IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom, 12/05/2019 . https://doi.org/10.1109/ICASSP.2019.8682648