Voice signal characteristics extracting method based on tensor decomposition
A voice signal and tensor decomposition technology, which is applied in voice analysis, voice recognition, instruments, etc., can solve the problem that voice features only contain part of the voice signal information, and achieve the effect of enhancing the representation ability
Inactive Publication Date: 2015-05-06
INNER MONGOLIA UNIV OF SCI & TECH +1
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[0009] The purpose of the present invention is to fully characterize the speech signal, and propose a speech signal feature extraction method based on tensor decomposition, which solves the problem that the traditional speech feature only contains part of the speech signal information
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[0039] The present invention will be described in detail below in conjunction with accompanying drawing and embodiment, also described the technical problem and beneficial effect that the technical solution of the present invention solves simultaneously, it should be pointed out that described embodiment is only intended to facilitate the understanding of the present invention, and It has no limiting effect on it.
[0040] Such as figure 1 Shown, the speech signal feature extraction method based on tensor decomposition of the present invention specifically comprises the following steps:
[0041] Step 1: The speech signal to be processed is divided into frames using a Hamming window, the frame length is L, and the frame shift is M, so that the speech signal is divided into N frames, and the frame sequence is obtained after sequential arrangement;
[0042] Step 2: Carry out R layer wavelet decomposition respectively to each frame speech signal after framing, because speech sign...
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The invention discloses a voice signal characteristics extracting method based on tensor decomposition and belongs to the technical field of voice signal processing. The voice signal characteristics extracting method based on the tensor decomposition comprises the following steps: having multi-layer wavelet decomposition to voice signals after framing, respectively extracting MR frequency cepstrum coefficients, the corresponding first order difference coefficient and second order difference coefficient from a plurality of component information after the wavelet decomposition to form a characteristic parameter vector, establishing a third order voice tensor and having tensor decomposition to the third order voice tensor, and calculating component information order and characteristic projection of characteristic parameters. Marticulated results are characteristics carried by each frame of voice signals. Compare with the traditional characteristic parameters, the voice signal characteristics extracting method based on the tensor decomposition has the advantages of enhancing representational ability to the voice signals, acquiring characteristics which carries more comprehensive voice signals, and improving the effects of voice signal processing systems such as voice identifying signal processing system, speaker identifying signal processing system.
Description
technical field [0001] The invention relates to a speech signal feature extraction method, in particular to a speech signal feature extraction method based on tensor decomposition, which belongs to the technical field of speech signal processing. Background technique [0002] Speech signal is a non-stationary time-varying signal, which carries various information. In speech signal processing such as speech coding, speech synthesis, speech recognition and speech enhancement, it is necessary to extract various information contained in speech. Generally speaking, the purpose of speech processing is to obtain certain speech feature parameters for efficient transmission or storage; or to achieve a certain purpose through certain processing operations, such as identifying the content of the speech, identifying the speaker, Synthetic speech, etc., simply put, is to conveniently and effectively extract and represent the information carried by the speech signal. [0003] With the c...
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IPC IPC(8): G10L15/02G10L19/032
Inventor 杨立东王晶
Owner INNER MONGOLIA UNIV OF SCI & TECH