Discontinuity detection in concatenated speech synthesis based on nonlinear speech analysis

Rhythm annotation is the basement of speech analysis and speech synthesis technology for Tibetan. Base on the study of Chinese rhythm, and Tibetan characters, this paper has studied the rhythm annotation for the speech synthesis technology of Tibetan, and has designed a set of rhythm annotation rules, which consists of Latin transliteration, tone type, syllable type, stress type, and break indices. Getting the annotative texts by Praat; then extracting prosodic parameters by Matlab, which is including the parameter designing, parameter extracting and results saving three parts.

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Review of Speech Analysis, Synthesis and Perception.

Contemporary Psychology ◽

10.1037/008323 ◽

1966 ◽

Vol 11 (3) ◽

pp. 141-141

Author(s):

No authorship indicated

Keyword(s):

Speech Analysis

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Speech synthesis from natural models by hand and by algorithm

PsycEXTRA Dataset ◽

10.1037/e520562012-289 ◽

2009 ◽

Author(s):

Robert E. Remez ◽

Kathryn R. Dubowski ◽

Morgana L. Davids ◽

Emily F. Thomas ◽

Nina Paddu ◽

...

Keyword(s):

Speech Synthesis

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189238 ◽

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.

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