IEICE TRANSACTIONS on Information
Mimicking Lombard Effect: An Analysis and Reconstruction
Summary :
Lombard speech is produced in noisy environments due to the Lombard effect and is intelligible in adverse environments. To adaptively control the intelligibility of transmitted speech for public announcement systems, in this study, we focus on perceptually mimicking Lombard speech under backgrounds with varying noise levels. Other approaches map corresponding neutral speech features to Lombard speech features, but as this can only be applied to one noise level at a time, it is unsuitable for varying noise levels because the characteristics of Lombard speech are varied according to noise level. Instead, we utilize a rule-based method that automatically generates rules and flexibly controls features with any change of noise level. Specifically, we conduct a feature tendency analysis and propose a continuous rule generation model to estimate the effect of varying noise levels on features. The proposed techniques, which are based on a coarticulation model, MRTD, and spectral-GMM, can easily modify neutral speech features by following the generated rules. Voices having these features are then synthesized by STRAIGHT to obtain Lombard speech fitting to noises with varying levels. To validate our proposed method, the quality of mimicking speech is evaluated in subjective listening experiments on similarity, intelligibility, and naturalness. In varying noise levels, the results show equal similarity with Lombard speech between the proposed method and a state-of-the-art method. Intelligibility and naturalness are comparable with some feature modifications.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.5 pp.1108-1117
- Publication Date
- 2020/05/01
- Publicized
- 2020/02/13
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019EDP7260
- Type of Manuscript
- PAPER
- Category
- Speech and Hearing
Authors
Thuan Van NGO
JAIST
Rieko KUBO
JAIST
Masato AKAGI
JAIST
Keyword
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Thuan Van NGO, Rieko KUBO, Masato AKAGI, "Mimicking Lombard Effect: An Analysis and Reconstruction" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 5, pp. 1108-1117, May 2020, doi: 10.1587/transinf.2019EDP7260.
Abstract: Lombard speech is produced in noisy environments due to the Lombard effect and is intelligible in adverse environments. To adaptively control the intelligibility of transmitted speech for public announcement systems, in this study, we focus on perceptually mimicking Lombard speech under backgrounds with varying noise levels. Other approaches map corresponding neutral speech features to Lombard speech features, but as this can only be applied to one noise level at a time, it is unsuitable for varying noise levels because the characteristics of Lombard speech are varied according to noise level. Instead, we utilize a rule-based method that automatically generates rules and flexibly controls features with any change of noise level. Specifically, we conduct a feature tendency analysis and propose a continuous rule generation model to estimate the effect of varying noise levels on features. The proposed techniques, which are based on a coarticulation model, MRTD, and spectral-GMM, can easily modify neutral speech features by following the generated rules. Voices having these features are then synthesized by STRAIGHT to obtain Lombard speech fitting to noises with varying levels. To validate our proposed method, the quality of mimicking speech is evaluated in subjective listening experiments on similarity, intelligibility, and naturalness. In varying noise levels, the results show equal similarity with Lombard speech between the proposed method and a state-of-the-art method. Intelligibility and naturalness are comparable with some feature modifications.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7260/_p
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@ARTICLE{e103-d_5_1108,
author={Thuan Van NGO, Rieko KUBO, Masato AKAGI, },
journal={IEICE TRANSACTIONS on Information},
title={Mimicking Lombard Effect: An Analysis and Reconstruction},
year={2020},
volume={E103-D},
number={5},
pages={1108-1117},
abstract={Lombard speech is produced in noisy environments due to the Lombard effect and is intelligible in adverse environments. To adaptively control the intelligibility of transmitted speech for public announcement systems, in this study, we focus on perceptually mimicking Lombard speech under backgrounds with varying noise levels. Other approaches map corresponding neutral speech features to Lombard speech features, but as this can only be applied to one noise level at a time, it is unsuitable for varying noise levels because the characteristics of Lombard speech are varied according to noise level. Instead, we utilize a rule-based method that automatically generates rules and flexibly controls features with any change of noise level. Specifically, we conduct a feature tendency analysis and propose a continuous rule generation model to estimate the effect of varying noise levels on features. The proposed techniques, which are based on a coarticulation model, MRTD, and spectral-GMM, can easily modify neutral speech features by following the generated rules. Voices having these features are then synthesized by STRAIGHT to obtain Lombard speech fitting to noises with varying levels. To validate our proposed method, the quality of mimicking speech is evaluated in subjective listening experiments on similarity, intelligibility, and naturalness. In varying noise levels, the results show equal similarity with Lombard speech between the proposed method and a state-of-the-art method. Intelligibility and naturalness are comparable with some feature modifications.},
keywords={},
doi={10.1587/transinf.2019EDP7260},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Mimicking Lombard Effect: An Analysis and Reconstruction
T2 - IEICE TRANSACTIONS on Information
SP - 1108
EP - 1117
AU - Thuan Van NGO
AU - Rieko KUBO
AU - Masato AKAGI
PY - 2020
DO - 10.1587/transinf.2019EDP7260
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2020
AB - Lombard speech is produced in noisy environments due to the Lombard effect and is intelligible in adverse environments. To adaptively control the intelligibility of transmitted speech for public announcement systems, in this study, we focus on perceptually mimicking Lombard speech under backgrounds with varying noise levels. Other approaches map corresponding neutral speech features to Lombard speech features, but as this can only be applied to one noise level at a time, it is unsuitable for varying noise levels because the characteristics of Lombard speech are varied according to noise level. Instead, we utilize a rule-based method that automatically generates rules and flexibly controls features with any change of noise level. Specifically, we conduct a feature tendency analysis and propose a continuous rule generation model to estimate the effect of varying noise levels on features. The proposed techniques, which are based on a coarticulation model, MRTD, and spectral-GMM, can easily modify neutral speech features by following the generated rules. Voices having these features are then synthesized by STRAIGHT to obtain Lombard speech fitting to noises with varying levels. To validate our proposed method, the quality of mimicking speech is evaluated in subjective listening experiments on similarity, intelligibility, and naturalness. In varying noise levels, the results show equal similarity with Lombard speech between the proposed method and a state-of-the-art method. Intelligibility and naturalness are comparable with some feature modifications.
ER -
English
