Reconciling pronunciation differences between the front-end and the back-end in the IBM speech synthesis system

2004 ◽  
Author(s):  
Wael Hamza ◽  
Ellen Eide ◽  
Raimo Bakis
Keyword(s):  
Speech Synthesis ◽  
Synthesis System ◽  
Front End

scholarly journals End-to-End Speech Synthesis for Tibetan Multidialect

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaona Xu ◽  
Li Yang ◽  
Yue Zhao ◽  
Hui Wang
Keyword(s):  
Speech Synthesis ◽  
Text Processing ◽  
Synthesis System ◽  
Sequence Structure ◽  
Training Corpus ◽  
Text Annotation ◽  
Front End ◽  
Cyclic Sequence ◽  
End To End ◽  
Tibetan Dialects

The research on Tibetan speech synthesis technology has been mainly focusing on single dialect, and thus there is a lack of research on Tibetan multidialect speech synthesis technology. This paper presents an end-to-end Tibetan multidialect speech synthesis model to realize a speech synthesis system which can be used to synthesize different Tibetan dialects. Firstly, Wylie transliteration scheme is used to convert the Tibetan text into the corresponding Latin letters, which effectively reduces the size of training corpus and the workload of front-end text processing. Secondly, a shared feature prediction network with a cyclic sequence-to-sequence structure is built, which maps the Latin transliteration vector of Tibetan character to Mel spectrograms and learns the relevant features of multidialect speech data. Thirdly, two dialect-specific WaveNet vocoders are combined with the feature prediction network, which synthesizes the Mel spectrum of Lhasa-Ü-Tsang and Amdo pastoral dialect into time-domain waveform, respectively. The model avoids using a large number of Tibetan dialect expertise for processing some time-consuming tasks, such as phonetic analysis and phonological annotation. Additionally, it can directly synthesize Lhasa-Ü-Tsang and Amdo pastoral speech on the existing text annotation. The experimental results show that the synthesized speech of Lhasa-Ü-Tsang and Amdo pastoral dialect based on our proposed method has better clarity and naturalness than the Tibetan monolingual model.

Research on Tibetan Language Synthesis System Front-End Text Processing Technology Based on HMM

2013 ◽  
Vol 411-414 ◽  
pp. 308-312
Author(s):  
Hong Zhi Yu ◽  
Jin Xi Zhang ◽  
Guang Rong Shan ◽  
Ning Ma
Keyword(s):  
Text Analysis ◽  
Speech Synthesis ◽  
Text Processing ◽  
Word Segmentation ◽  
Processing Technology ◽  
Synthesis System ◽  
Front End ◽  
Text Word ◽  
Solid Foundation ◽  
Rhythm Analysis

The standardization of the text, word segmentation, the basic stitching unit divided for rhythm analysis and pronunciation conversion is an important content of the speech synthesis system front-end text processing modules. Lhasa Tibetan language and voice characteristics proposed the implementation of a set of Tibetan speech synthesis text analysis module to analyze and describe the Lhasa Tibetan language layer information and maps voice layer. The completion of the study is to lay a solid foundation for further Tibetan speech synthesis system.

Development of a demisyllable-based speech synthesis system

2003 ◽  
Author(s):  
S.J. Eady ◽  
T.M.S. Hemphill ◽  
J.R. Woolsey ◽  
J.A.W. Clayards
Keyword(s):  
Speech Synthesis ◽  
Synthesis System

Effects of Speech Synthesis on the Proofreading Efficiency of Postsecondary Students with Learning Disabilities

1995 ◽  
Vol 18 (2) ◽  
pp. 141-158 ◽  
Author(s):  
Marshall H. Raskind ◽  
Eleanor Higgins
Keyword(s):  
Learning Disabilities ◽  
Speech Synthesis ◽  
Written Language ◽  
The Other ◽  
Read Aloud ◽  
Synthesis Condition ◽  
Superior Performance ◽  
Postsecondary Students ◽  
Students With Learning Disabilities ◽  
Synthesis System

This study investigated the effects of speech synthesis on the proofreading efficiency of postsecondary students with learning disabilities. Subjects proofread self-generated written language samples under three conditions: (a) using a speech synthesis system that simultaneously highlighted and “spoke” words on a computer monitor, (b) having the text read aloud to them by another person, and (c) receiving no assistance. Using the speech synthesis system enabled subjects to detect a significantly higher percentage of total errors than either of the other two proofreading conditions. In addition, subjects were able to locate a significantly higher percentage of capitalization, spelling, usage and typographical errors under the speech synthesis condition. However, having the text read aloud by another person significantly outperformed the other conditions in finding “grammar-mechanical” errors. Results are discussed with regard to underlying reasons for the overall superior performance of the speech synthesis system and the implications of using speech synthesis as a compensatory writing aid for postsecondary students with learning disabilities.

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