The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste artigo propomos um método denominado “mascaramento de ruído contínuo (cNM)” que permite eliminar zumbidos residuais em um vocoder contínuo, ou seja, em que todos os parâmetros são contínuos e oferece um sistema simples e flexível de análise e síntese de fala. Os codificadores de voz paramétricos tradicionais geralmente mostram uma deterioração perceptível na qualidade da fala sintetizada devido a diferentes algoritmos de processamento. Além disso, uma ressíntese de ruído imprecisa (por exemplo, soprosidade ou rouquidão) também é considerada uma das principais causas subjacentes à degradação do desempenho, levando a transientes ruidosos e descontinuidade temporal na fala sintetizada. Para superar esses problemas, um novo cNM é desenvolvido baseado no desvio de distorção de fase, a fim de reduzir o efeito perceptual do ruído residual, permitindo uma reconstrução adequada das características do ruído, e modelar melhor os segmentos de voz rangentes que podem ocorrer na fala natural. Para este fim, o cNM é projetado para manter apenas os componentes de voz sob uma condição de limiar do cNM enquanto descarta outros. Avaliamos a abordagem proposta e comparamos com vocoders de última geração usando testes de audição objetivos e subjetivos. Resultados experimentais mostram que o método proposto pode reduzir o efeito do ruído residual e pode alcançar a qualidade de outras abordagens sofisticadas como STRAIGHT e modelo de pulso de domínio logarítmico (PML).
Mohammed Salah AL-RADHI
Budapest University of Technology and Economics
Tamás Gábor CSAPÓ
Budapest University of Technology and Economics,MTA-ELTE Lendület Lingual Articulation Research Group
Géza NÉMETH
Budapest University of Technology and Economics
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Mohammed Salah AL-RADHI, Tamás Gábor CSAPÓ, Géza NÉMETH, "Continuous Noise Masking Based Vocoder for Statistical Parametric Speech Synthesis" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 5, pp. 1099-1107, May 2020, doi: 10.1587/transinf.2019EDP7167.
Abstract: 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).
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7167/_p
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@ARTICLE{e103-d_5_1099,
author={Mohammed Salah AL-RADHI, Tamás Gábor CSAPÓ, Géza NÉMETH, },
journal={IEICE TRANSACTIONS on Information},
title={Continuous Noise Masking Based Vocoder for Statistical Parametric Speech Synthesis},
year={2020},
volume={E103-D},
number={5},
pages={1099-1107},
abstract={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).},
keywords={},
doi={10.1587/transinf.2019EDP7167},
ISSN={1745-1361},
month={May},}
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TY - JOUR
TI - Continuous Noise Masking Based Vocoder for Statistical Parametric Speech Synthesis
T2 - IEICE TRANSACTIONS on Information
SP - 1099
EP - 1107
AU - Mohammed Salah AL-RADHI
AU - Tamás Gábor CSAPÓ
AU - Géza NÉMETH
PY - 2020
DO - 10.1587/transinf.2019EDP7167
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2020
AB - 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).
ER -