Quadratic phase interpolation for voiced speech synthesis in MBE model

1993 ◽  
Vol 29 (10) ◽  
pp. 856-857 ◽  
Author(s):  
H. Yang ◽  
S.-N. Koh ◽  
P. Sivaprakasapillai
Keyword(s):  
Speech Synthesis ◽  
Quadratic Phase ◽  
Voiced Speech ◽  
Phase Interpolation

A New Algorithm for Noisy Speech Classification Based on GMM

2014 ◽  
Vol 8 (1) ◽  
pp. 508-511
Author(s):  
Zhongbao Chen ◽  
Zhigang Fang ◽  
Jie Xu ◽  
Pengying Du ◽  
Xiaoping Luo
Keyword(s):  
Speech Signal ◽  
Speech Synthesis ◽  
Gaussian Mixture ◽  
Synthetic Speech ◽  
Noisy Environments ◽  
The Arts ◽  
Voiced Speech ◽  
Signal Simulation ◽  
Speech Classification ◽  
Increasing Demand

Speech can be broadly categorized into voiceless, voiced, and mute signal, in which voiced speech can be further classified into vowel and voiced consonant. With the ever increasing demand of the speech synthesis applications, it is urgent to develop an effective classification method to differentiate vowel and voiced consonant signal since they are two distinct components that affect the naturalness of the synthetic speech signal. State-of-the-arts algorithms for speech signal classification are effective in classifying voiceless, voiced and mute speech signal, however, not effective in further classifying the voiced signal. In view of the issue, a new algorithm for speech classification based on Gaussian Mixture Model (GMM) is proposed, which can directly classify a speech into voiceless, voiced consonant, vowel and mute signal. Simulation results demonstrate that the proposed algorithm is effective even under the noisy environments.

A New Method for Noisy Speech Classification Based on Gaussian Mixture Models

2012 ◽  
Vol 532-533 ◽  
pp. 1253-1257
Author(s):  
Li Hai Yao ◽  
Jie Xu ◽  
Hao Jiang
Keyword(s):  
Speech Signal ◽  
Speech Synthesis ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Noisy Environments ◽  
The Arts ◽  
Voiced Speech ◽  
Speech Feature ◽  
Speech Classification ◽  
Increasing Demand

Speech can be broadly categorized into voiceless, voiced, and mute signal, in which voiced speech can be further classified into vowel and voiced consonant. With the ever increasing demand of the speech synthesis applications, it is urgent to develop an effective classification method to differentiate vowel and voiced consonant signal since they are two distinct components that affect the naturalness of the synthetic speech signal. State-of-the-arts algorithms for speech signal classification are effective in classifying voiceless, voiced and mute speech signal, however, not effective in further classifying the voiced signal. In view of the issue, a new algorithm for speech classification based on Gaussian Mixture Model (GMM) is proposed, which can directly classify a speech into voiceless, voiced consonant, vowel and mute signal. Specifically, a new speech feature is proposed, and the GMM is also modified for speech classification. Simulation results demonstrate that the proposed algorithm is effective even under the noisy environments.

Two-phase unwrapping criteria for quadratic and cubic interpolation in voiced speech synthesis

1995 ◽  
Vol 78 (2) ◽  
pp. 227-238
Author(s):  
HAIYUN YANG ◽  
SOO-NGEE KOH ◽  
P. STVAPRAKASAPILLAI
Keyword(s):  
Speech Synthesis ◽  
Phase Unwrapping ◽  
Two Phase ◽  
Voiced Speech

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.

Sign in / Sign up

Export Citation Format

Share Document