Speech signal dynamic characteristic extraction method based on formant curves

Abstract

The invention provides a speech signal dynamic characteristic extraction method based on formant curves, belonging to the technical field of Chinese speech signal dynamic characteristic extraction. The method comprises the following steps: acquiring speech signals; carrying out preprocessingon the speech signals; extracting formant frequency characteristics of the speech signals; according to the sequence from the first frame to the last frame, combining the first formant frequency characteristic values of all the frames of preprocessed speech signals to obtain a first formant curve, and then obtaining a second formant curve, a third formant curve, and a fourth formant curve in the same manner; carrying out rapid Fourier transform on each obtained formant curve to obtain a linear frequency spectrum; obtaining an energy spectrum according to the linear frequency spectrum; obtaining logarithm energy according to the energy spectrum; and carrying out discrete cosine transform on the logarithm energy. Compared with the existing method, the method provided by the invention has the advantages that the speech signal dynamic characteristics are extracted, the temporal correlation is available, therefore, the close relevance before and after the speech signals and between the adjacent speech signals is disclosed, and the speech recognition property is improved.

Description

technical field [0001] The invention belongs to the technical field of dynamic feature extraction of Chinese speech signals, in particular to a method for extracting dynamic features of speech signals based on formant curves. Background technique [0002] Speech recognition research in my country started in the 1950s, but it did not develop rapidly until the 1970s. The Chinese Academy of Sciences, Tsinghua University, Peking University and many other research institutes are engaged in the development of Chinese speech recognition system. At present, the research on continuous speech recognition system with large vocabulary is close to the highest level in foreign countries; "In the plan, the research on Chinese speech recognition has received strong support. The National 863 "Intelligent Computer Topics" expert group has specifically established a project for speech recognition research. At the same time, due to China's growing international status and its important position...

AI Technical Summary

Problems solved by technology

With the development of recognition technology, people found that the characteristic parameters in the time domain are not very stable and distinguishable, so they began to use the frequency domain parameters as the characteristics of the speech signal, such as pitch period, formant frequency, linear prediction coefficient ( LPC), line spectrum pair (LSP), cepstral coefficient, etc., the most widely used feature parameter is the Mel cepstral coefficient (MFCC) based on the human auditory model; but once these parameters are applied to the noise environment, their performance will drop sharply;

[0005] Moreover, the characteristic parameters mentioned above all reflect the static characteristics of the speech. The dynamic characteristics of the speech signal refer to the characteristic parameters extracted from several consecutive frames of speech. Acceleration parameters cannot fully mine the dynamic information, so they cannot reflect the dynamic characteristics of the speech signal well

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