HMM-Based Maximum Likelihood Frame Alignment for Voice Conversion from a Nonparallel Corpus
Summary :
One of the problems associated with voice conversion from a nonparallel corpus is how to find the best match or alignment between the source and the target vector sequences without linguistic information. In a previous study, alignment was achieved by minimizing the distance between the source vector and the transformed vector. This method, however, yielded a sequence of feature vectors that were not well matched with the underlying speaker model. In this letter, the vectors were selected from the candidates by maximizing the overall likelihood of the selected vectors with respect to the target model in the HMM context. Both objective and subjective evaluations were carried out using the CMU ARCTIC database to verify the effectiveness of the proposed method.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.12 pp.3064-3067
- Publication Date
- 2017/12/01
- Publicized
- 2017/08/23
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDL8144
- Type of Manuscript
- LETTER
- Category
- Speech and Hearing
Authors
Ki-Seung LEE
Konkuk University
Keyword
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Ki-Seung LEE, "HMM-Based Maximum Likelihood Frame Alignment for Voice Conversion from a Nonparallel Corpus" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 12, pp. 3064-3067, December 2017, doi: 10.1587/transinf.2017EDL8144.
Abstract: One of the problems associated with voice conversion from a nonparallel corpus is how to find the best match or alignment between the source and the target vector sequences without linguistic information. In a previous study, alignment was achieved by minimizing the distance between the source vector and the transformed vector. This method, however, yielded a sequence of feature vectors that were not well matched with the underlying speaker model. In this letter, the vectors were selected from the candidates by maximizing the overall likelihood of the selected vectors with respect to the target model in the HMM context. Both objective and subjective evaluations were carried out using the CMU ARCTIC database to verify the effectiveness of the proposed method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDL8144/_p
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@ARTICLE{e100-d_12_3064,
author={Ki-Seung LEE, },
journal={IEICE TRANSACTIONS on Information},
title={HMM-Based Maximum Likelihood Frame Alignment for Voice Conversion from a Nonparallel Corpus},
year={2017},
volume={E100-D},
number={12},
pages={3064-3067},
abstract={One of the problems associated with voice conversion from a nonparallel corpus is how to find the best match or alignment between the source and the target vector sequences without linguistic information. In a previous study, alignment was achieved by minimizing the distance between the source vector and the transformed vector. This method, however, yielded a sequence of feature vectors that were not well matched with the underlying speaker model. In this letter, the vectors were selected from the candidates by maximizing the overall likelihood of the selected vectors with respect to the target model in the HMM context. Both objective and subjective evaluations were carried out using the CMU ARCTIC database to verify the effectiveness of the proposed method.},
keywords={},
doi={10.1587/transinf.2017EDL8144},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - HMM-Based Maximum Likelihood Frame Alignment for Voice Conversion from a Nonparallel Corpus
T2 - IEICE TRANSACTIONS on Information
SP - 3064
EP - 3067
AU - Ki-Seung LEE
PY - 2017
DO - 10.1587/transinf.2017EDL8144
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2017
AB - One of the problems associated with voice conversion from a nonparallel corpus is how to find the best match or alignment between the source and the target vector sequences without linguistic information. In a previous study, alignment was achieved by minimizing the distance between the source vector and the transformed vector. This method, however, yielded a sequence of feature vectors that were not well matched with the underlying speaker model. In this letter, the vectors were selected from the candidates by maximizing the overall likelihood of the selected vectors with respect to the target model in the HMM context. Both objective and subjective evaluations were carried out using the CMU ARCTIC database to verify the effectiveness of the proposed method.
ER -
English
