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2017 | OriginalPaper | Chapter

Quality Improvement of Vietnamese HMM-Based Speech Synthesis System Based on Decomposition of Naturalness and Intelligibility Using Non-negative Matrix Factorization

Authors : Anh-Tuan Dinh, Thanh-Son Phan, Masato Akagi

Published in: Advances in Information and Communication Technology

Publisher: Springer International Publishing

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Abstract

Hidden Markov model (HMM)-based synthesized speech is intelligible but not natural especially under limited data condition. The goal of this study is to improve naturalness without violating acceptable intelligibility by decomposing the naturalness and intelligibility of synthesized speech using a novel asymmetric bilinear model involving non-negative matrix factorization (NMF). Subjective evaluations carried out on Vietnamese data confirmed that the achieved synthesis quality is higher than other methods under limited data condition. Since F0 contour is important for naturalness and intelligibility, especially in Vietnamese. Proposed method is capable of modifying over-smoothed F0 contour without destroying tonal information.

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Metadata
Title
Quality Improvement of Vietnamese HMM-Based Speech Synthesis System Based on Decomposition of Naturalness and Intelligibility Using Non-negative Matrix Factorization
Authors
Anh-Tuan Dinh
Thanh-Son Phan
Masato Akagi
Copyright Year
2017
Book
Advances in Information and Communication Technology

Print ISBN: 978-3-319-49072-4

Electronic ISBN: 978-3-319-49073-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-49073-1_53

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