Application of Feature Extraction in Text-to-Speech Processing
A speech signal synthesis in real time, with an unlimited vocabulary is very complicated task for all languages. The synthesizers usually work in the frequency domain and the fundamental frequency F0 and duration must be determined for all phonemes or diphones by conventional equipment based on linguistic rules . Our effort is to minimize the difference between the synthetic speech of the synthesizer, which is usually more monotonous, and the natural speech of people. Because of this, a special functional block is included into the synthesizer for prosody control. A multilayer artificial neural network (ANN) is used for prosody control in our case. In this part of the synthesizer the fundamental frequency is “a little bit” modified in such a way that speech can sound as natural as possible.
The number of input training parameters for ANN training must be generally kept as small as possible because of the optimal generalization ability of the network. An original method for the determination of the most important features (input parameters) for the training of ANN for prosody control is described in this paper. This method is based on the “data mining” from the database of the training patterns by the GUHA method described in .
KeywordsFundamental Frequency Speech Signal Natural Speech Synthetic Speech Elementary Conjunction
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