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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/13703

Title: Study on Quality Improvement of HMM-Based Synthesized Voices Using Asymmetric Bilinear Model
Authors: Dinh-Anh, Tuan
Morikawa, Daisuke
Akagi Masato
Issue Date: 2016-07
Publisher: 信号処理学会
Magazine name: Journal of Signal Processing
Volume: 20
Number: 4
Start page: 205
End page: 208
DOI: 10.2299/jsp.20.205
Abstract: Hidden Markov model (HMM)-based synthesized voices are intelligible but not natural especially under limited-data conditions due to over-smoothed speech spectra. Improving naturalness is a critical problem of HMM-based speech synthesis. One solution is to use voice conversion techniques to convert over-smoothed spectra to natural spectra. Although conventional conversion methods transform speech spectra to natural ones to improve naturalness, they cause unexpected distortions in the intelligibility of synthesized speech. The aim of the study is to improve naturalness without reducing the intelligibility of synthesized speech by employing our novel asymmetric bilinear model (ABM) to separate the intelligibility and naturalness of synthesized speech. In the study, our ABM was implemented on the modulation spectrum domain of Mel-cepstral coefficient (MCC) sequences to enhance the fine structure of spectral parameter trajectory generated from HMMs. Subjective evaluations carried out on English data confirmed that the achieved naturalness of the method using the ABM involving singular value decomposition (SVD) was competitive with other methods under large-data conditions and outperformed other methods under limited-data conditions. Moreover, modified rhyme test (MRT) showed that the intelligibility of synthesized speech was well preserved with our method.
Rights: Copyright (C) 2016 信号処理学会. Tuan Dinh-Anh, Daisuke Morikawa, Masato Akagi, Journal of Signal Processing, 20(4), 2016, 205-208. http://dx.doi.org/10.2299/jsp.20.205
URI: http://hdl.handle.net/10119/13703
Material Type: publisher
Appears in Collections:b10-1. 雑誌掲載論文 (Journal Articles)

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