NDLI: A novel unit selection method for concatenation speech system using similarity measure

Content Provider	IEEE Xplore Digital Library
Author	Ran Zhang Jianhua Tao Ya Li Zhengqi Wen
Copyright Year	2013
Description	Author affiliation: Inst. of Autom., Beijing, China (Ran Zhang; Jianhua Tao; Ya Li; Zhengqi Wen)
Abstract	This paper presents a new approach to unit selection for corpus-based TTS system, in which the units are selected according to their similarity with synthetic target generated by a parametric synthesizer. In the training stage, a group of classifiers are trained based on human perceptual judgments. The outputs of the classifiers are used to make a distinction rather than using traditional methods such as continuously-valued cost. In order to obtain a better classification result, different combinations of features are tried as input vectors, and the similarity rating is carried out dexterously. Subjective listening tests on a Mandarin female TTS system show that the proposed classifier based speech synthesis system outperforms the traditional unit-selection system.
Starting Page	1
Ending Page	5
File Size	350852
Page Count	5
File Format	PDF
DOI	10.1109/ICSDA.2013.6709846
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-25
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training speech synthesis hybird Hidden Markov models Speech Acoustics Vectors Speech synthesis target cost unit selection Context modeling
Content Type	Text
Resource Type	Article

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