Lightweight LPCNet-Based Neural Vocoder with Tensor Decomposition

By Hiroki Kanagawa, Yusuke Ijima. 2020

9 citations recorded by the source corpus.

This paper proposes a lightweight neural vocoder based on LPCNet. The recently proposed LPCNet exploits linear predictive coding to represent vocal tract characteristics, and can rapidly synthesize high-quality waveforms with fewer parameters than WaveRNN. For even greater speeds, it is necessary to reduce the time-heavy two GRUs and the DualFC. Although the original work only pruned the first GRU weight, there is room for improvements in the other GRU and DualFC. Accord-ingly, we use tensor decomposition to reduce these remaining parameters by more than 80%. For the proposed method we demonstrate that 1) it is 1.26 times faster on a CPU, and 2) it matched naturalness of the original LPCNet for acoustic features extracted from natural speech and for those predicted by TTS.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xu Tan
2. Tao Qin
3. Tie-Yan Liu
4. Frank K. Soong
5. Yi Ren
6. Olivier Chapelle
7. Yanqing Liu
8. Rongjie Huang

Citations in the corpus

• A Survey on Neural Speech Synthesis (2021, methodology): “Lightweight Model WaveRNN [147], LightSpeech [214] SqueezeWave [426], [148, 114, 123, 424]”