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Publication . Article . 2020

Continuous Noise Masking Based Vocoder for Statistical Parametric Speech Synthesis

Mohammed Salah Al-Radhi; Tamás Gábor Csapó; Géza Németh;
Open Access
Published: 10 Feb 2020 Journal: IEICE Transactions on Information and Systems, volume E103.D, issue 5, pages 1,099-1,107 (issn: 0916-8532, eissn: 1745-1361, Copyright policy )
In this article, we propose a method called “continuous noise masking (cNM)” that allows eliminating residual buzziness in a continuous vocoder, i.e. of which all parameters are continuous and offers a simple and flexible speech analysis and synthesis system. Traditional parametric vocoders generally show a perceptible deterioration in the quality of the synthesized speech due to different processing algorithms. Furthermore, an inaccurate noise resynthesis (e.g. in breathiness or hoarseness) is also considered to be one of the main underlying causes of performance degradation, leading to noisy transients and temporal discontinuity in the synthesized speech. To overcome these issues, a new cNM is developed based on the phase distortion deviation in order to reduce the perceptual effect of the residual noise, allowing a proper reconstruction of noise characteristics, and model better the creaky voice segments that may happen in natural speech. To this end, the cNM is designed to keep only voice components under a condition of the cNM threshold while discarding others. We evaluate the proposed approach and compare with state-of-the-art vocoders using objective and subjective listening tests. Experimental results show that the proposed method can reduce the effect of residual noise and can reach the quality of other sophisticated approaches like STRAIGHT and log domain pulse model (PML).
Subjects by Vocabulary

Microsoft Academic Graph classification: Speech recognition Speech synthesis computer.software_genre computer Masking (art) Parametric statistics Computer science Continuous noise


noise masking, continuous vocoder, speech synthesis, phase distortion, kernel density functions, Artificial Intelligence, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Hardware and Architecture, Software

Funded by
A European AI On Demand Platform and Ecosystem
  • Funder: European Commission (EC)
  • Project Code: 825619
  • Funding stream: H2020 | RIA
Validated by funder