Title
NeuralDPS: Neural Deterministic Plus Stochastic Model With Multiband Excitation for Noise-Controllable Waveform Generation
Abstract
The traditional vocoders have the advantages of high synthesis efficiency, strong interpretability, and speech editability, while the neural vocoders have the advantage of high synthesis quality. To combine the advantages of two vocoders, inspired by the traditional deterministic plus stochastic model, this paper proposes a novel neural vocoder named NeuralDPS which can retain high speech quality and acquire high synthesis efficiency and noise controllability. Firstly, this framework contains four modules: a deterministic source module, a stochastic source module, a neural V/UV decision module and a neural filter module. The input required by the vocoder is just the spectral parameter, which avoids the error caused by estimating additional parameters, such as F0. Secondly, to solve the problem that different frequency bands may have different proportions of deterministic components and stochastic components, a multiband excitation strategy is used to generate a more accurate excitation signal and reduce the neural filter’s burden. Thirdly, a method to control noise components of speech is proposed. In this way, the signal-to-noise ratio (SNR) of speech can be adjusted easily. Objective and subjective experimental results show that our proposed NeuralDPS vocoder can obtain similar performance with the WaveNet and it generates waveforms at least 280 times faster than the WaveNet vocoder. It is also 28% faster than WaveGAN’s synthesis efficiency on a single CPU core. We have also verified through experiments that this method can effectively control the noise components in the predicted speech and adjust the SNR of speech.
Year
DOI
Venue
2022
10.1109/TASLP.2022.3140480
IEEE/ACM Transactions on Audio, Speech, and Language Processing
Keywords
DocType
Volume
Vocoder,speech synthesis,deterministic plus stochastic,multiband excitation,noise control
Journal
30
Issue
ISSN
Citations 
1
2329-9290
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Tao Wang100.34
Ruibo Fu215.11
Jiangyan Yi31917.99
Jianhua Tao4848138.00
Zhengqi Wen58624.41