We are very excited to join forces with OctoML.ai! Contact Grigori Fursin for more details!

A comparison of recent waveform generation and acoustic modeling methods for neural-network-based speech synthesis

lib:a7bb239fcf959d8b (v1.0.0)

Authors: Xin Wang,Jaime Lorenzo-Trueba,Shinji Takaki,Lauri Juvela,Junichi Yamagishi
ArXiv: 1804.02549
Document:  PDF  DOI 
Abstract URL: http://arxiv.org/abs/1804.02549v1

Recent advances in speech synthesis suggest that limitations such as the lossy nature of the amplitude spectrum with minimum phase approximation and the over-smoothing effect in acoustic modeling can be overcome by using advanced machine learning approaches. In this paper, we build a framework in which we can fairly compare new vocoding and acoustic modeling techniques with conventional approaches by means of a large scale crowdsourced evaluation. Results on acoustic models showed that generative adversarial networks and an autoregressive (AR) model performed better than a normal recurrent network and the AR model performed best. Evaluation on vocoders by using the same AR acoustic model demonstrated that a Wavenet vocoder outperformed classical source-filter-based vocoders. Particularly, generated speech waveforms from the combination of AR acoustic model and Wavenet vocoder achieved a similar score of speech quality to vocoded speech.

Relevant initiatives  

Related knowledge about this paper Reproduced results (crowd-benchmarking and competitions) Artifact and reproducibility checklists Common formats for research projects and shared artifacts Reproducibility initiatives


Please log in to add your comments!
If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!