Embedding a Differentiable Mel-Cepstral Synthesis Filter to a Neural Speech Synthesis System
Takenori Yoshimura, Shinji Takaki, Kazuhiro Nakamura, Keiichiro Oura, Yukiya Hono, Kei Hashimoto, Yoshihiko Nankaku, Keiichi Tokuda
Abstract
This paper integrates a classic mel-cepstral synthesis filter into a modern neural speech synthesis system towards end-to-end controllable speech synthesis. Since the mel-cepstral synthesis filter is explicitly embedded in neural waveform models in the proposed system, both voice characteristics and the pitch of synthesized speech are highly controlled via a frequency warping parameter and fundamental frequency, respectively. We implement the mel-cepstral synthesis filter as a differentiable and GPU-friendly module to enable the acoustic and waveform models in the proposed system to be simultaneously optimized in an end-to-end manner. Experiments show that the proposed system improves speech quality from a baseline system maintaining controllability. The core PyTorch modules used in the experiments are publicly available on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
BibTeX
@inproceedings{icassp2023_embeddingadiffer,
title = {Embedding a Differentiable Mel-Cepstral Synthesis Filter to a Neural Speech Synthesis System},
author = {Takenori Yoshimura and Shinji Takaki and Kazuhiro Nakamura and Keiichiro Oura and Yukiya Hono and Kei Hashimoto and Yoshihiko Nankaku and Keiichi Tokuda},
booktitle = {ICASSP 2023},
year = {2023}
}