DocumentCode
2132820
Title
Implementation and evaluation of statistical parametric speech synthesis methods for the Persian language
Author
Bahaadini, Sara ; Sameti, Hossein ; Khorram, Soheil
Author_Institution
Dept. of Comput. Eng., Sharif Univ. of Technol., Tehran, Iran
fYear
2011
fDate
18-21 Sept. 2011
Firstpage
1
Lastpage
6
Abstract
Scattered and little research in the field of Persian speech synthesis systems has been performed during the last ten years. Comprehensive framework that properly implements and adapts statistical speech synthesis methods for Persian has not been conducted yet. In this paper, recent statistical parametric speech synthesis methods including CLUSTERGEN, traditional HMM-based speech synthesis and its STRAIGHT version, are implemented and adapted for Persian language. CCR test is carried out to compare these methods with each other and with unit selection method. Listeners Score samples based on CMOS. The methods were ranked by averaging the CCR scores. The results show that STRAIGHT-based system produces the best quality. Traditional HMM-based and unit selection are second and third in quality ranking. These approximately produce the same quality. Finally CLUSTERGEN produces the worst quality among these four systems.
Keywords
hidden Markov models; natural language processing; speech synthesis; CLUSTERGEN; HMM-based speech synthesis; Persian language; Persian speech synthesis system; quality ranking; statistical parametric speech synthesis; statistical speech synthesis; unit selection; Adaptation models; CMOS integrated circuits; Databases; Hidden Markov models; Speech; Speech synthesis; Training; CCR test; Persian language; speech synthesis; statistical parametric; text to speech;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning for Signal Processing (MLSP), 2011 IEEE International Workshop on
Conference_Location
Santander
ISSN
1551-2541
Print_ISBN
978-1-4577-1621-8
Electronic_ISBN
1551-2541
Type
conf
DOI
10.1109/MLSP.2011.6064608
Filename
6064608
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