Prosodic phrasing for speech synthesis of written telecommunications by the deaf

2002 ◽  
Author(s):  
J. Bachenko ◽  
E. Fitzpatrick
Keyword(s):  
Speech Synthesis ◽  
Prosodic Phrasing

Exploiting Morphological and Phonological Features to Improve Prosodic Phrasing for Mongolian Speech Synthesis

2021 ◽  
Vol 29 ◽  
pp. 274-285
Author(s):  
Rui Liu ◽  
Berrak Sisman ◽  
Feilong Bao ◽  
Jichen Yang ◽  
Guanglai Gao ◽  
...  
Keyword(s):  
Speech Synthesis ◽  
Prosodic Phrasing ◽  
Phonological Features

Speech synthesis from natural models by hand and by algorithm

2009 ◽  
Author(s):  
Robert E. Remez ◽  
Kathryn R. Dubowski ◽  
Morgana L. Davids ◽  
Emily F. Thomas ◽  
Nina Paddu ◽  
...  
Keyword(s):  
Speech Synthesis

Design of English text-to-speech conversion algorithm based on machine learning

2020 ◽  
pp. 1-12
Author(s):  
Li Dongmei
Keyword(s):  
Machine Learning ◽  
Speech Synthesis ◽  
Feature Recognition ◽  
Learning Algorithm ◽  
Morphological Structure ◽  
English Text ◽  
Text To Speech ◽  
Part Of Speech ◽  
Modern Computer ◽  
Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

Integrating Articulatory Information in Deep Learning-Based Text-to-Speech Synthesis

2017 ◽  
Author(s):  
Beiming Cao ◽  
Myungjong Kim ◽  
Jan van Santen ◽  
Ted Mau ◽  
Jun Wang
Keyword(s):  
Deep Learning ◽  
Speech Synthesis ◽  
Text To Speech ◽  
Text To Speech Synthesis

A Template-Based Approach for Speech Synthesis Intonation Generation Using LSTMs

2016 ◽  
Author(s):  
Srikanth Ronanki ◽  
Gustav Eje Henter ◽  
Zhizheng Wu ◽  
Simon King
Keyword(s):  
Speech Synthesis
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